Head of Applied AI Engineering

Recruiting from Scratch | AI, Healthcare, Life Sciences

2h 46m ago

Remote In-Country

Full Time

Lead

Israel, United States of America

$300k - $365k USD/yr

Who is Recruiting from Scratch:

Recruiting from Scratch is a premier talent firm that focuses on placing the best product managers, software, and hardware talent at innovative companies. Our team is 100% remote and we work with teams across the United States to help them hire.

Head of Applied AI Engineering

Location

United States — Remote, working US time zones
Israel — Remote, working Israel hours

Remote-first role with approximately 10–20% travel and quarterly team offsites in New York. No relocation support.

Company Stage of Funding

Growth-stage, profitable AI company

Founded: 2017
Total Funding: $50M+
Team Size: Approximately 100 employees
Industry: AI, Healthcare, Life Sciences
Business Scale: $100M+ revenue, with an ambition to scale to $500M.

Office Type

Remote-first. Candidates must be based in the United States and work US time zones, or be based in Israel and work Israel hours. The company maintains a small New York office and holds quarterly team offsites in New York. Expect approximately 10–20% travel to support enterprise clients, team collaboration, and strategic business needs.

Salary

$300,000–$365,000 base salary.

The company is flexible on compensation and may go higher for exceptional candidates. In addition to base salary, the role includes an annual performance-based bonus and competitive equity.

Equity

Competitive equity package.

The company offers periodic secondary equity sales approximately every 18–24 months, allowing employees to sell a portion of their vested shares before an IPO or other liquidity event. The stated program allows employees to sell up to 20% of vested shares during these events, subject to the applicable program terms.

Visa

Open to visa transfers, including OPT and H-1B transfers.

Candidates must be able to work from the United States in US time zones or from Israel in Israel hours. No relocation support is available.

Employment Type

Full-time

Company Description

This company is building an AI execution platform for healthcare, life sciences, and wellness. Its mission is to turn fragmented healthcare data into precise, measurable action within the enterprise systems organizations already use.

Healthcare organizations have access to extensive patient data, treatments, and scientific research, but executing on that information remains difficult. The platform connects data, AI, and operational workflows to help healthcare enterprises move from insight to action.

The company supports healthcare organizations, life sciences companies, and wellness businesses across complex workflows. Its work spans patient engagement, scheduling and routing, conversational AI, patient recruitment for clinical trials, and predictive targeting. Rather than simply providing dashboards or analytics, the platform integrates AI capabilities into existing enterprise systems to drive measurable outcomes.

The company reports supporting more than 190 million patients and members, generating over $2 billion in measurable value, and contributing to the development and launch of more than 50 life-changing drugs. It is a profitable, growth-stage business with a significant opportunity to expand its impact as healthcare organizations adopt AI-driven execution.

The organization has approximately 100 employees and operates across AI, healthcare, and life sciences. Its technical stack includes Python, TypeScript, Node.js, SQL, PostgreSQL, Redis, LangChain, LangGraph, AWS, S3, Kubernetes, SageMaker, Spark, Iceberg, ClickHouse, Anthropic, and Amazon Bedrock. Its work also involves healthcare systems such as EHRs and EMRs, with an emphasis on HIPAA and enterprise-grade data handling.

The company is entrepreneurial and ownership-driven. It values high-agency builders who move quickly, operate with low ego, and take responsibility for both product outcomes and business impact. Engineers are expected to make meaningful architectural decisions, work directly with customers, and translate complex business needs into production AI systems.

The Head of Applied AI Engineering will establish and lead a new Applied AI, or forward-deployed engineering, group. This person will be a hands-on technical leader who sets the standard for how applied AI engineers build and deploy systems while growing a high-performing team.

The role is intentionally weighted toward individual technical contribution: approximately 75% hands-on engineering and 25% people leadership. The ideal candidate is an experienced engineer and manager who can build production-grade agentic systems, work directly with strategic enterprise clients, and help scale the company's AI capabilities alongside its business.

What You Will Do

1. Build and Ship Production AI Systems

Spend approximately 75% of your time building, designing, and shipping production AI systems.
Set the technical standard for how forward-deployed engineers approach architecture, implementation, testing, deployment, and iteration.
Design and build agentic AI systems that solve complex operational problems in healthcare and life sciences.
Develop production-grade LLM applications that integrate with enterprise systems and support real customer workflows.
Build systems that move beyond prototypes and demonstrations into reliable, measurable production use.
Make pragmatic architectural decisions that balance speed, reliability, scalability, and business value.
Establish engineering patterns and implementation practices that the broader Applied AI Engineering team can adopt.
Remain deeply hands-on while providing technical direction to other engineers.

2. Develop Agentic AI and Conversational Systems

Build agentic AI systems and LLM-powered applications for enterprise healthcare workflows.
Develop voice agents and multimodal conversational systems for use cases such as patient scheduling, routing, and engagement.
Integrate conversational AI with client enterprise systems, operational workflows, and existing data infrastructure.
Use technologies such as Python, TypeScript, Node.js, LangChain, and LangGraph to build and orchestrate AI-powered applications.
Design systems that can reason over relevant information, execute actions, and interact reliably with external services.
Improve the reliability, accuracy, observability, and performance of AI agents operating in production.
Evaluate LLM capabilities, orchestration approaches, and system architectures to determine the right solution for each use case.
Ensure AI systems are designed for real-world enterprise constraints, including data access, privacy, reliability, and operational requirements.

3. Build Predictive AI and Machine Learning Systems

Develop and ship predictive models that turn large-scale healthcare data into actionable outcomes.
Build precision-targeting capabilities using the company's approximately 1.2 petabytes of proprietary data.
Apply machine learning and data science to use cases such as identifying suitable patients for Phase II and Phase III clinical trials.
Work with large, complex datasets to develop useful predictions and support operational decisions.
Build the infrastructure and workflows required to deploy, monitor, and improve predictive models in production.
Collaborate across data science, engineering, and customer-facing teams to translate business problems into ML solutions.
Evaluate model performance against practical outcomes, not just offline metrics.
Ensure predictive systems can integrate into existing enterprise workflows and support measurable customer impact.

4. Integrate AI Into Enterprise Healthcare Systems

Build AI applications that connect with existing healthcare, life sciences, and enterprise systems.
Integrate solutions with EHRs, EMRs, databases, APIs, and other client technology infrastructure.
Design service integrations that allow AI systems to access relevant information and execute actions within existing workflows.
Work with technologies such as PostgreSQL, Redis, SQL, AWS, and S3 to support data access and application requirements.
Build secure, reliable integrations that account for enterprise constraints and operational dependencies.
Understand customer workflows and identify where AI can improve execution, efficiency, and outcomes.
Work with client teams to navigate technical complexity, data availability, and system integration requirements.
Ensure solutions are practical to deploy, maintain, and scale across enterprise environments.

5. Establish and Lead the Applied AI Engineering Team

Build and lead a new forward-deployed Applied AI Engineering group.
Recruit, hire, mentor, and develop a high-performing team of Applied AI Engineers.
Spend approximately 25% of your time on people leadership, team development, and organizational responsibilities.
Demonstrate what excellent forward-deployed engineering looks like through your own technical work.
Set expectations for engineering quality, ownership, speed, customer engagement, and production execution.
Help engineers develop strong technical judgment and the ability to solve ambiguous customer problems.
Provide feedback, coaching, and support as the team scales.
Make thoughtful decisions about team structure, hiring priorities, performance, and development.
Build a culture that combines technical excellence, low ego, accountability, and commercial awareness.
Create reusable practices that allow the group to grow without sacrificing engineering quality or execution speed.

6. Serve as the Technical Executive for Strategic Clients

Act as a senior technical partner for strategic enterprise healthcare and life sciences customers.
Work directly with customer executives, technical stakeholders, and operational leaders to understand business needs.
Translate complex enterprise requirements into practical AI architectures and implementation plans.
Lead technical discussions and help customers understand how AI systems can address their most important operational challenges.
Support pre-sales conversations, technical evaluations, and expansion opportunities.
Help shape customer-specific roadmaps and identify opportunities to expand product adoption.
Build credibility with enterprise stakeholders through technical depth, clear communication, and an understanding of business outcomes.
Balance customer-specific needs with reusable product capabilities and scalable engineering practices.
Ensure that technical solutions deliver measurable value and support long-term customer relationships.

7. Support Commercial Growth and Enterprise Expansion

Partner with commercial and customer-facing teams to support strategic deals.
Participate in pre-sales technical discussions and help demonstrate the practical value of the platform.
Translate customer problems into compelling technical solutions and implementation approaches.
Identify opportunities to expand existing engagements through additional AI capabilities and workflows.
Help customers understand technical feasibility, implementation requirements, and expected outcomes.
Connect engineering decisions to customer value, adoption, and business growth.
Contribute to the company's ambition to grow from more than $100 million in revenue toward $500 million.
Help build a repeatable approach to delivering high-value AI solutions across healthcare and life sciences organizations.

8. Shape Technical Strategy and Engineering Culture

Influence the architecture and technical direction of the company's Applied AI capabilities.
Evaluate frameworks, cloud services, data platforms, and AI tooling based on real production requirements.
Establish reusable patterns for building, deploying, and maintaining agentic systems.
Use modern AI development tools, including Claude Code, Gemini, and other AI-assisted engineering tools, to improve productivity.
Encourage experimentation while maintaining high standards for correctness, security, and reliability.
Make decisions that support both immediate customer delivery and longer-term platform development.
Collaborate with senior leadership to align engineering priorities with product and business objectives.
Help establish a culture of high agency, rapid execution, strong ownership, and continuous improvement.

Ideal Candidate Background

Experience Requirements

10+ years of hands-on software engineering experience.
At least a couple of years of people management experience.
Strong background in applied AI, machine learning, data science, or production AI engineering.
Experience taking AI/ML systems from experimentation through production deployment.
Experience building agentic systems, LLM applications, or other AI-powered products.
Demonstrated ability to remain hands-on while leading engineers and technical initiatives.
Experience working with enterprise customers or solving complex customer-specific technical problems.
Experience in healthcare, health insurance, health systems, life sciences, or pharmaceuticals.
Entrepreneurial startup experience, ideally with exposure to scaling a company through significant growth.
Strong communication skills and executive presence, with the ability to engage both technical teams and senior business stakeholders.
Ability to connect technical implementation to customer outcomes and commercial objectives.

Education Requirements

No specific degree or university requirement has been provided.
Prioritize demonstrated engineering ability, production AI/ML experience, technical leadership, and business impact over educational credentials alone.
Relevant experience in machine learning, data science, computer science, or applied AI can be valuable, but the ability to build and lead production systems is the central requirement.

Applied AI and Machine Learning

Strong understanding of machine learning, data science, and applied AI.
Experience building AI/ML systems that have been deployed and used in production.
Ability to identify appropriate AI approaches for complex business and operational problems.
Experience evaluating model performance, reliability, and real-world effectiveness.
Familiarity with the full lifecycle of AI systems, from experimentation and prototyping through deployment and ongoing improvement.
Ability to work with large datasets and translate data into actionable predictions or decisions.
Experience balancing model quality, latency, cost, and operational constraints.
Strong judgment when deciding whether to use traditional ML, LLMs, agentic systems, or a combination of approaches.

LLMs and Agentic Systems

Experience building production LLM-powered applications.
Familiarity with agentic architectures and systems that use models to reason, orchestrate tasks, or interact with external services.
Experience with frameworks such as LangChain or LangGraph, or comparable orchestration tools.
Experience building conversational AI, voice agents, multimodal applications, or workflow automation systems is valuable.
Understanding of how to integrate LLMs with APIs, databases, enterprise systems, and external tools.
Ability to build reliable AI workflows with appropriate validation, error handling, monitoring, and fallback mechanisms.
Experience evaluating AI outputs and addressing issues such as hallucinations, inconsistent behavior, and unpredictable execution.
Strong practical understanding of how to move AI prototypes into robust production systems.

Technical Stack and Software Engineering

Strong Python proficiency.
Experience with TypeScript and Node.js is valuable for building application and service layers.
Experience designing and developing production APIs and backend services.
Familiarity with SQL and relational databases such as PostgreSQL.
Experience with Redis or comparable technologies for caching, state, or application infrastructure.
Familiarity with AWS and services such as S3, Bedrock, or SageMaker.
Experience with containerized and cloud-native applications.
Familiarity with Kubernetes or other orchestration infrastructure.
Experience working with large-scale data processing technologies such as Spark, Iceberg, or ClickHouse is valuable.
Ability to design reliable systems that integrate AI models, data pipelines, and enterprise services.
Strong software engineering fundamentals, including testing, debugging, observability, and production operations.

Data Science and Large-Scale Data

Experience working with large, complex datasets.
Understanding of data preparation, feature engineering, model evaluation, and production inference.
Ability to build predictive models that support real business or operational outcomes.
Experience with large-scale data processing and storage systems.
Familiarity with Spark, Iceberg, ClickHouse, or comparable data technologies is a plus.
Ability to work with sensitive data and understand the operational requirements of data-intensive enterprise systems.
Experience translating data science work into deployable applications and customer-facing capabilities.
Strong judgment when balancing analytical rigor with practical implementation needs.

Healthcare, Life Sciences, and Enterprise Systems

Experience in healthcare, health insurance, health systems, life sciences, pharmaceuticals, or a closely related industry.
Understanding of complex healthcare or life sciences workflows.
Experience integrating software with enterprise systems, databases, and APIs.
Familiarity with EHR/EMR systems is valuable.
Understanding of healthcare data privacy, security, and compliance requirements.
Familiarity with HIPAA or other relevant healthcare data protection requirements.
Experience building systems that operate within enterprise security and governance constraints.
Ability to work with customers who have complex technical environments and specialized operational requirements.
Experience in clinical trial recruitment, patient engagement, scheduling, routing, or healthcare operations is particularly relevant.

People Leadership and Team Building

At least a couple of years of experience managing engineers or technical teams.
Experience recruiting, mentoring, and developing engineering talent.
Ability to establish clear expectations and create a high-performing team environment.
Comfortable making difficult people decisions when necessary.
Experience balancing individual contribution with team leadership responsibilities.
Ability to provide technical guidance without becoming a bottleneck.
Strong coaching and feedback skills.
Experience scaling engineering teams or establishing a new function is valuable.
Ability to maintain a high technical bar while building an accountable, collaborative culture.

Hands-On Technical Leadership

Willing to spend approximately 75% of working time building and shipping systems directly.
Able to lead by example through code, architecture, debugging, and production execution.
Comfortable handling difficult technical problems personally rather than delegating all implementation.
Able to make architecture decisions and explain the reasoning behind them.
Can set technical direction while remaining close to implementation details.
Enjoys building real systems alongside a team.
Can move between coding, technical design, customer discussions, and team leadership.
Avoids becoming exclusively a people manager or strategy leader.

Enterprise Customer and Commercial Skills

Experience working directly with enterprise customers or strategic accounts.
Ability to lead technical conversations with senior customer stakeholders.
Strong communication skills and executive presence.
Comfortable explaining complex technical concepts to both technical and non-technical audiences.
Ability to understand customer requirements and translate them into technical solutions.
Experience supporting pre-sales, technical evaluations, or enterprise expansion is valuable.
Understands the connection between engineering delivery, customer outcomes, adoption, and revenue.
Can navigate challenging customer situations with confidence, humility, and good judgment.
Able to build trust while maintaining realistic technical commitments.

Startup Experience and Entrepreneurial Mindset

Experience in an entrepreneurial or high-growth startup environment.
Ideally, has stayed with a company through a significant period of growth.
Exposure to scaling a business beyond $100 million in revenue is a strong advantage.
Comfortable operating with ambiguity and making decisions without extensive process.
High agency, strong initiative, and a bias toward action.
Willing to take ownership of problems beyond a narrowly defined job description.
Able to balance speed, customer needs, technical quality, and commercial impact.
Motivated by building a team and product rather than simply managing an established function.
Low ego and comfortable learning from engineers, customers, and business leaders.

Executive Presence and Communication

Can confidently lead technical discussions with senior enterprise stakeholders.
Communicates clearly and credibly in high-stakes customer conversations.
Able to influence decisions without relying on hierarchy.
Comfortable presenting technical approaches and trade-offs to executives.
Balances confidence with humility and openness to feedback.
Can navigate both boardroom-level conversations and detailed engineering discussions.
Understands when to go deep technically and when to focus on business outcomes.
Builds relationships through credibility, responsiveness, and sound judgment.

Nice-to-Have Qualifications

Experience building voice agents or multimodal conversational AI.
Experience with LangChain, LangGraph, Anthropic, or Amazon Bedrock.
Experience with AWS SageMaker, S3, and Kubernetes.
Experience working with large-scale data systems such as Spark, Iceberg, and ClickHouse.
Experience with healthcare AI, clinical workflows, or patient recruitment.
Familiarity with EHR/EMR integrations and HIPAA-related requirements.
Experience building predictive targeting or patient-matching models.
Experience in health insurance, pharma, life sciences, or healthcare operations.
Experience working as a forward-deployed engineer or in a customer-facing technical role.
Experience establishing a new engineering team or scaling an applied AI function.
Experience supporting enterprise pre-sales and account expansion.
Experience scaling a startup through a major revenue growth phase.
Experience using AI coding tools such as Claude Code and Gemini to accelerate engineering work.

Compensation & Benefits

Base salary of $300,000–$365,000, with flexibility to go higher for exceptional candidates.
Annual performance-based bonus; the company indicates approximately 10% of total compensation as a reference point.
Competitive equity package.
Periodic secondary equity liquidity events approximately every 18–24 months, allowing employees to sell a portion of vested shares under the applicable program.
Remote-first working environment for eligible candidates in the United States or Israel.
Access to modern AI development tools, including Claude Code, Gemini, and other AI engineering tools.
Quarterly team offsites in New York.
High autonomy and ownership over architectural and product decisions.
Opportunity to build and lead a new Applied AI Engineering function.
Direct exposure to strategic enterprise healthcare and life sciences customers.
Opportunity to contribute to a profitable AI company with substantial scale and ambitious growth plans.
Approximately 10–20% travel, depending on customer and team needs.

Why Join

Build and lead a new Applied AI Engineering organization: Establish the forward-deployed engineering function and define how the team builds, deploys, and scales production AI systems.
Remain deeply hands-on: Spend approximately 75% of your time building real systems while shaping technical standards and mentoring engineers.
Solve meaningful healthcare problems: Apply AI and machine learning to workflows that affect patients, healthcare providers, insurers, and life sciences organizations.
Work with large-scale proprietary data: Build predictive systems and precision-targeting capabilities using approximately 1.2 petabytes of data.
Work on production agentic AI: Develop LLM applications, voice agents, and multimodal conversational systems that integrate with enterprise infrastructure.
Influence business growth: Work directly with strategic customers and help support enterprise deals, adoption, and expansion.
Operate with high ownership: Join a culture that values initiative, low ego, rapid execution, and direct responsibility for outcomes.
Shape technical direction: Make meaningful architectural and product decisions rather than working within a narrowly defined engineering scope.
Join a profitable, scaled AI company: Help take an established business from more than $100 million in revenue toward its next stage of growth.
Access meaningful equity opportunities: Participate in competitive equity compensation and periodic secondary liquidity events.
Build a team with lasting impact: Recruit, mentor, and develop engineers while establishing the standards for an important new technical function.

Required Skills

pythontypescriptnode_jslangchainlanggraphpostgresqlredisaws

Required Languages

🇬🇧 English

Head of Applied AI Engineering

Recruiting from Scratch | AI, Healthcare, Life Sciences

2h 46m ago

Remote In-Country

Full Time

Lead

Israel, United States of America

$300k - $365k USD/yr

Who is Recruiting from Scratch:

Head of Applied AI Engineering

Location

United States — Remote, working US time zones
Israel — Remote, working Israel hours

Remote-first role with approximately 10–20% travel and quarterly team offsites in New York. No relocation support.

Company Stage of Funding

Growth-stage, profitable AI company

Founded: 2017
Total Funding: $50M+
Team Size: Approximately 100 employees
Industry: AI, Healthcare, Life Sciences
Business Scale: $100M+ revenue, with an ambition to scale to $500M.

Office Type

Salary

$300,000–$365,000 base salary.

The company is flexible on compensation and may go higher for exceptional candidates. In addition to base salary, the role includes an annual performance-based bonus and competitive equity.

Equity

Competitive equity package.

Visa

Open to visa transfers, including OPT and H-1B transfers.

Candidates must be able to work from the United States in US time zones or from Israel in Israel hours. No relocation support is available.

Employment Type

Full-time

Company Description

What You Will Do

1. Build and Ship Production AI Systems

Spend approximately 75% of your time building, designing, and shipping production AI systems.
Set the technical standard for how forward-deployed engineers approach architecture, implementation, testing, deployment, and iteration.
Design and build agentic AI systems that solve complex operational problems in healthcare and life sciences.
Develop production-grade LLM applications that integrate with enterprise systems and support real customer workflows.
Build systems that move beyond prototypes and demonstrations into reliable, measurable production use.
Make pragmatic architectural decisions that balance speed, reliability, scalability, and business value.
Establish engineering patterns and implementation practices that the broader Applied AI Engineering team can adopt.
Remain deeply hands-on while providing technical direction to other engineers.

2. Develop Agentic AI and Conversational Systems

Build agentic AI systems and LLM-powered applications for enterprise healthcare workflows.
Develop voice agents and multimodal conversational systems for use cases such as patient scheduling, routing, and engagement.
Integrate conversational AI with client enterprise systems, operational workflows, and existing data infrastructure.
Use technologies such as Python, TypeScript, Node.js, LangChain, and LangGraph to build and orchestrate AI-powered applications.
Design systems that can reason over relevant information, execute actions, and interact reliably with external services.
Improve the reliability, accuracy, observability, and performance of AI agents operating in production.
Evaluate LLM capabilities, orchestration approaches, and system architectures to determine the right solution for each use case.
Ensure AI systems are designed for real-world enterprise constraints, including data access, privacy, reliability, and operational requirements.

3. Build Predictive AI and Machine Learning Systems

Develop and ship predictive models that turn large-scale healthcare data into actionable outcomes.
Build precision-targeting capabilities using the company's approximately 1.2 petabytes of proprietary data.
Apply machine learning and data science to use cases such as identifying suitable patients for Phase II and Phase III clinical trials.
Work with large, complex datasets to develop useful predictions and support operational decisions.
Build the infrastructure and workflows required to deploy, monitor, and improve predictive models in production.
Collaborate across data science, engineering, and customer-facing teams to translate business problems into ML solutions.
Evaluate model performance against practical outcomes, not just offline metrics.
Ensure predictive systems can integrate into existing enterprise workflows and support measurable customer impact.

4. Integrate AI Into Enterprise Healthcare Systems

Build AI applications that connect with existing healthcare, life sciences, and enterprise systems.
Integrate solutions with EHRs, EMRs, databases, APIs, and other client technology infrastructure.
Design service integrations that allow AI systems to access relevant information and execute actions within existing workflows.
Work with technologies such as PostgreSQL, Redis, SQL, AWS, and S3 to support data access and application requirements.
Build secure, reliable integrations that account for enterprise constraints and operational dependencies.
Understand customer workflows and identify where AI can improve execution, efficiency, and outcomes.
Work with client teams to navigate technical complexity, data availability, and system integration requirements.
Ensure solutions are practical to deploy, maintain, and scale across enterprise environments.

5. Establish and Lead the Applied AI Engineering Team

Build and lead a new forward-deployed Applied AI Engineering group.
Recruit, hire, mentor, and develop a high-performing team of Applied AI Engineers.
Spend approximately 25% of your time on people leadership, team development, and organizational responsibilities.
Demonstrate what excellent forward-deployed engineering looks like through your own technical work.
Set expectations for engineering quality, ownership, speed, customer engagement, and production execution.
Help engineers develop strong technical judgment and the ability to solve ambiguous customer problems.
Provide feedback, coaching, and support as the team scales.
Make thoughtful decisions about team structure, hiring priorities, performance, and development.
Build a culture that combines technical excellence, low ego, accountability, and commercial awareness.
Create reusable practices that allow the group to grow without sacrificing engineering quality or execution speed.

6. Serve as the Technical Executive for Strategic Clients

Act as a senior technical partner for strategic enterprise healthcare and life sciences customers.
Work directly with customer executives, technical stakeholders, and operational leaders to understand business needs.
Translate complex enterprise requirements into practical AI architectures and implementation plans.
Lead technical discussions and help customers understand how AI systems can address their most important operational challenges.
Support pre-sales conversations, technical evaluations, and expansion opportunities.
Help shape customer-specific roadmaps and identify opportunities to expand product adoption.
Build credibility with enterprise stakeholders through technical depth, clear communication, and an understanding of business outcomes.
Balance customer-specific needs with reusable product capabilities and scalable engineering practices.
Ensure that technical solutions deliver measurable value and support long-term customer relationships.

7. Support Commercial Growth and Enterprise Expansion

Partner with commercial and customer-facing teams to support strategic deals.
Participate in pre-sales technical discussions and help demonstrate the practical value of the platform.
Translate customer problems into compelling technical solutions and implementation approaches.
Identify opportunities to expand existing engagements through additional AI capabilities and workflows.
Help customers understand technical feasibility, implementation requirements, and expected outcomes.
Connect engineering decisions to customer value, adoption, and business growth.
Contribute to the company's ambition to grow from more than $100 million in revenue toward $500 million.
Help build a repeatable approach to delivering high-value AI solutions across healthcare and life sciences organizations.

8. Shape Technical Strategy and Engineering Culture

Influence the architecture and technical direction of the company's Applied AI capabilities.
Evaluate frameworks, cloud services, data platforms, and AI tooling based on real production requirements.
Establish reusable patterns for building, deploying, and maintaining agentic systems.
Use modern AI development tools, including Claude Code, Gemini, and other AI-assisted engineering tools, to improve productivity.
Encourage experimentation while maintaining high standards for correctness, security, and reliability.
Make decisions that support both immediate customer delivery and longer-term platform development.
Collaborate with senior leadership to align engineering priorities with product and business objectives.
Help establish a culture of high agency, rapid execution, strong ownership, and continuous improvement.

Ideal Candidate Background

Experience Requirements

10+ years of hands-on software engineering experience.
At least a couple of years of people management experience.
Strong background in applied AI, machine learning, data science, or production AI engineering.
Experience taking AI/ML systems from experimentation through production deployment.
Experience building agentic systems, LLM applications, or other AI-powered products.
Demonstrated ability to remain hands-on while leading engineers and technical initiatives.
Experience working with enterprise customers or solving complex customer-specific technical problems.
Experience in healthcare, health insurance, health systems, life sciences, or pharmaceuticals.
Entrepreneurial startup experience, ideally with exposure to scaling a company through significant growth.
Strong communication skills and executive presence, with the ability to engage both technical teams and senior business stakeholders.
Ability to connect technical implementation to customer outcomes and commercial objectives.

Education Requirements

No specific degree or university requirement has been provided.
Prioritize demonstrated engineering ability, production AI/ML experience, technical leadership, and business impact over educational credentials alone.
Relevant experience in machine learning, data science, computer science, or applied AI can be valuable, but the ability to build and lead production systems is the central requirement.

Applied AI and Machine Learning

Strong understanding of machine learning, data science, and applied AI.
Experience building AI/ML systems that have been deployed and used in production.
Ability to identify appropriate AI approaches for complex business and operational problems.
Experience evaluating model performance, reliability, and real-world effectiveness.
Familiarity with the full lifecycle of AI systems, from experimentation and prototyping through deployment and ongoing improvement.
Ability to work with large datasets and translate data into actionable predictions or decisions.
Experience balancing model quality, latency, cost, and operational constraints.
Strong judgment when deciding whether to use traditional ML, LLMs, agentic systems, or a combination of approaches.

LLMs and Agentic Systems

Experience building production LLM-powered applications.
Familiarity with agentic architectures and systems that use models to reason, orchestrate tasks, or interact with external services.
Experience with frameworks such as LangChain or LangGraph, or comparable orchestration tools.
Experience building conversational AI, voice agents, multimodal applications, or workflow automation systems is valuable.
Understanding of how to integrate LLMs with APIs, databases, enterprise systems, and external tools.
Ability to build reliable AI workflows with appropriate validation, error handling, monitoring, and fallback mechanisms.
Experience evaluating AI outputs and addressing issues such as hallucinations, inconsistent behavior, and unpredictable execution.
Strong practical understanding of how to move AI prototypes into robust production systems.

Technical Stack and Software Engineering

Strong Python proficiency.
Experience with TypeScript and Node.js is valuable for building application and service layers.
Experience designing and developing production APIs and backend services.
Familiarity with SQL and relational databases such as PostgreSQL.
Experience with Redis or comparable technologies for caching, state, or application infrastructure.
Familiarity with AWS and services such as S3, Bedrock, or SageMaker.
Experience with containerized and cloud-native applications.
Familiarity with Kubernetes or other orchestration infrastructure.
Experience working with large-scale data processing technologies such as Spark, Iceberg, or ClickHouse is valuable.
Ability to design reliable systems that integrate AI models, data pipelines, and enterprise services.
Strong software engineering fundamentals, including testing, debugging, observability, and production operations.

Data Science and Large-Scale Data

Experience working with large, complex datasets.
Understanding of data preparation, feature engineering, model evaluation, and production inference.
Ability to build predictive models that support real business or operational outcomes.
Experience with large-scale data processing and storage systems.
Familiarity with Spark, Iceberg, ClickHouse, or comparable data technologies is a plus.
Ability to work with sensitive data and understand the operational requirements of data-intensive enterprise systems.
Experience translating data science work into deployable applications and customer-facing capabilities.
Strong judgment when balancing analytical rigor with practical implementation needs.

Healthcare, Life Sciences, and Enterprise Systems

Experience in healthcare, health insurance, health systems, life sciences, pharmaceuticals, or a closely related industry.
Understanding of complex healthcare or life sciences workflows.
Experience integrating software with enterprise systems, databases, and APIs.
Familiarity with EHR/EMR systems is valuable.
Understanding of healthcare data privacy, security, and compliance requirements.
Familiarity with HIPAA or other relevant healthcare data protection requirements.
Experience building systems that operate within enterprise security and governance constraints.
Ability to work with customers who have complex technical environments and specialized operational requirements.
Experience in clinical trial recruitment, patient engagement, scheduling, routing, or healthcare operations is particularly relevant.

People Leadership and Team Building

At least a couple of years of experience managing engineers or technical teams.
Experience recruiting, mentoring, and developing engineering talent.
Ability to establish clear expectations and create a high-performing team environment.
Comfortable making difficult people decisions when necessary.
Experience balancing individual contribution with team leadership responsibilities.
Ability to provide technical guidance without becoming a bottleneck.
Strong coaching and feedback skills.
Experience scaling engineering teams or establishing a new function is valuable.
Ability to maintain a high technical bar while building an accountable, collaborative culture.

Hands-On Technical Leadership

Willing to spend approximately 75% of working time building and shipping systems directly.
Able to lead by example through code, architecture, debugging, and production execution.
Comfortable handling difficult technical problems personally rather than delegating all implementation.
Able to make architecture decisions and explain the reasoning behind them.
Can set technical direction while remaining close to implementation details.
Enjoys building real systems alongside a team.
Can move between coding, technical design, customer discussions, and team leadership.
Avoids becoming exclusively a people manager or strategy leader.

Enterprise Customer and Commercial Skills

Experience working directly with enterprise customers or strategic accounts.
Ability to lead technical conversations with senior customer stakeholders.
Strong communication skills and executive presence.
Comfortable explaining complex technical concepts to both technical and non-technical audiences.
Ability to understand customer requirements and translate them into technical solutions.
Experience supporting pre-sales, technical evaluations, or enterprise expansion is valuable.
Understands the connection between engineering delivery, customer outcomes, adoption, and revenue.
Can navigate challenging customer situations with confidence, humility, and good judgment.
Able to build trust while maintaining realistic technical commitments.

Startup Experience and Entrepreneurial Mindset

Experience in an entrepreneurial or high-growth startup environment.
Ideally, has stayed with a company through a significant period of growth.
Exposure to scaling a business beyond $100 million in revenue is a strong advantage.
Comfortable operating with ambiguity and making decisions without extensive process.
High agency, strong initiative, and a bias toward action.
Willing to take ownership of problems beyond a narrowly defined job description.
Able to balance speed, customer needs, technical quality, and commercial impact.
Motivated by building a team and product rather than simply managing an established function.
Low ego and comfortable learning from engineers, customers, and business leaders.

Executive Presence and Communication

Can confidently lead technical discussions with senior enterprise stakeholders.
Communicates clearly and credibly in high-stakes customer conversations.
Able to influence decisions without relying on hierarchy.
Comfortable presenting technical approaches and trade-offs to executives.
Balances confidence with humility and openness to feedback.
Can navigate both boardroom-level conversations and detailed engineering discussions.
Understands when to go deep technically and when to focus on business outcomes.
Builds relationships through credibility, responsiveness, and sound judgment.

Nice-to-Have Qualifications

Experience building voice agents or multimodal conversational AI.
Experience with LangChain, LangGraph, Anthropic, or Amazon Bedrock.
Experience with AWS SageMaker, S3, and Kubernetes.
Experience working with large-scale data systems such as Spark, Iceberg, and ClickHouse.
Experience with healthcare AI, clinical workflows, or patient recruitment.
Familiarity with EHR/EMR integrations and HIPAA-related requirements.
Experience building predictive targeting or patient-matching models.
Experience in health insurance, pharma, life sciences, or healthcare operations.
Experience working as a forward-deployed engineer or in a customer-facing technical role.
Experience establishing a new engineering team or scaling an applied AI function.
Experience supporting enterprise pre-sales and account expansion.
Experience scaling a startup through a major revenue growth phase.
Experience using AI coding tools such as Claude Code and Gemini to accelerate engineering work.

Compensation & Benefits

Base salary of $300,000–$365,000, with flexibility to go higher for exceptional candidates.
Annual performance-based bonus; the company indicates approximately 10% of total compensation as a reference point.
Competitive equity package.
Periodic secondary equity liquidity events approximately every 18–24 months, allowing employees to sell a portion of vested shares under the applicable program.
Remote-first working environment for eligible candidates in the United States or Israel.
Access to modern AI development tools, including Claude Code, Gemini, and other AI engineering tools.
Quarterly team offsites in New York.
High autonomy and ownership over architectural and product decisions.
Opportunity to build and lead a new Applied AI Engineering function.
Direct exposure to strategic enterprise healthcare and life sciences customers.
Opportunity to contribute to a profitable AI company with substantial scale and ambitious growth plans.
Approximately 10–20% travel, depending on customer and team needs.

Why Join

Build and lead a new Applied AI Engineering organization: Establish the forward-deployed engineering function and define how the team builds, deploys, and scales production AI systems.
Remain deeply hands-on: Spend approximately 75% of your time building real systems while shaping technical standards and mentoring engineers.
Solve meaningful healthcare problems: Apply AI and machine learning to workflows that affect patients, healthcare providers, insurers, and life sciences organizations.
Work with large-scale proprietary data: Build predictive systems and precision-targeting capabilities using approximately 1.2 petabytes of data.
Work on production agentic AI: Develop LLM applications, voice agents, and multimodal conversational systems that integrate with enterprise infrastructure.
Influence business growth: Work directly with strategic customers and help support enterprise deals, adoption, and expansion.
Operate with high ownership: Join a culture that values initiative, low ego, rapid execution, and direct responsibility for outcomes.
Shape technical direction: Make meaningful architectural and product decisions rather than working within a narrowly defined engineering scope.
Join a profitable, scaled AI company: Help take an established business from more than $100 million in revenue toward its next stage of growth.
Access meaningful equity opportunities: Participate in competitive equity compensation and periodic secondary liquidity events.
Build a team with lasting impact: Recruit, mentor, and develop engineers while establishing the standards for an important new technical function.

Required Skills

pythontypescriptnode_jslangchainlanggraphpostgresqlredisaws

Required Languages

🇬🇧 English