Middle Technical Project Manager, Applied AI (Contract)

About LILT

AI is changing how the world communicates — and LILT is leading that transformation.

We're on a mission to make the world's information accessible to everyone, regardless of the language they speak. We use cutting-edge AI, machine translation, and human-in-the-loop expertise to translate content faster, more accurately, and more cost-effectively without compromising on brand, voice, or quality.

At LILT, we empower our teammates with leading tools, global collaboration, and growth opportunities to do their best work. Our company virtues—Work together, win together; Find a way or make one; Dance in the customer's shoes; Quicker than they expect; Quality is Job 1—guide everything we do. We are trusted by Intel Corporation, Canva, the United States Department of Defense, the United States Air Force, ASICS, and hundreds of global Enterprises. Backed by Sequoia, Intel Capital, and Redpoint, we’re building a category-defining company in a $50B+ global translation market being redefined by AI.

About the Role

We are looking for a Technical Project Manager to join our Applied AI team to lead data programs that help improve automatic speech recognition (ASR) for low-resource and long-tail languages—making speech technology more accurate and accessible for communities underserved by existing AI systems.
Four-month fixed-term contract, requiring 4–6 hours per day, with potential for extension.

You will own end-to-end program delivery, bringing together multilingual teams, language expertise, and technical capabilities to meet customer goals. This is a high-ownership, outcomes-driven role with accountability for quality, timelines, and budget, and the opportunity to build scalable approaches to complex AI data challenges.

This role requires high cross-functional aptitude and a singular focus on getting things done. Successful candidates will be able to demonstrate a history of making significant demands of a business and aligning stakeholders to a project’s success.

Key Responsibilities

1. Program and Volume Delivery

• Delivery planning: Own end-to-end program plans, from pilot and calibration through scaled production and final delivery, aligning scope, staffing, and milestones with customer requirements.

• Tracking and forecasting: Monitor output against volume and timeline targets, forecast delivery using actual performance, and adjust capacity or workflows to address gaps.

• Program milestones: Lead readiness reviews, quality checkpoints, and acceptance milestones, coordinating stakeholders and driving timely decisions.

• Budget and margin: Manage delivery costs against budget and margin targets; proactively flag scope changes, cost pressures, and resource trade-offs.

2. Team Leadership and Capacity Building

• Build and lead teams: Assemble multilingual delivery teams with the expertise needed for complex AI data programs.

• Align capacity: Align team capacity and capabilities with program goals.

• Enable performance: Foster strong performance through clear expectations, training, and feedback.

3. Quality and Data Handling

• Drive data quality: Ensure AI datasets meet customer standards and support meaningful improvements in model capabilities.

• Lead continuous improvement: Collaborate with language experts, Quality, and Research to improve data consistency and delivery performance.

• Uphold responsible data practices: Maintain high standards for privacy, confidentiality, and secure handling of customer data.

4. Reporting and Stakeholder Communication

• Build customer trust: Serve as a key delivery contact alongside the account team, aligning expectations and maintaining strong customer relationships.

• Communicate program performance: Translate progress, risks, and trade-offs into clear, actionable updates for customers and internal stakeholders.

• Drive alignment and decisions: Bring cross-functional teams together to resolve issues, manage changing requirements, and keep programs moving toward shared goals.

5. Tooling and Process

• Partner with Engineering: Translate program needs into tooling and automation improvements that enable efficient, high-quality AI data delivery.

• Build for scale: Develop reusable processes and playbooks that capture lessons learned, accelerate future launches, and strengthen delivery across language programs.

Qualifications

Essential Skills and Experience

• Experience: 3+ years managing high-volume, deadline-driven service delivery in data annotation, transcription, localization, content operations, BPO or a comparable field.

• Team leadership: Demonstrated success leading distributed teams to meet demanding delivery and quality goals.

• Analytical skills: Strong ability to use data, dashboards, and forecasting to guide decisions on delivery, quality, and cost.

• Quality: Working knowledge of quality assurance for AI training data, including evaluation, sampling, and acceptance criteria.

• Communication: Excellent written English and the ability to write unambiguous instructions for multilingual contributors.

• Adaptability: Comfortable bringing structure to ambiguous requirements and leading programs in evolving environments.

• Availability: Able to commit for the full contract term.

Preferred Qualifications

• Audio data: Experience with speech or audio datasets, ASR training data, or transcription.

• Global delivery: Experience building or managing multilingual teams across international markets.

• Data stewardship: Experience delivering programs with privacy, confidentiality, and secure data-handling requirements.

• Tools: Familiarity with annotation platforms and Jira.

• Languages: Fluency in an additional language; experience working with low-resource languages is a plus.

Our Story

Our founders, Spence and John met at Google working on Google Translate. As researchers at Stanford and Berkeley, they both worked on language technology to make information accessible to everyone. While together at Google, they were amazed to learn that Google Translate wasn’t used for enterprise products and services inside the company.The quality just wasn’t there. So they set out to build something better. LILT was born.

LILT has been a machine learning company since its founding in 2015. At the time, machine translation didn’t meet the quality standard for enterprise translations, so LILT assembled a cutting-edge research team tasked with closing that gap. While meeting customer demand for translation services, LILT has prioritized investments in Large Language Models, human-in-the-loop systems, and now agentic AI.

With AI innovation accelerating and enterprise demand growing, the next phase of LILT’s journey is just beginning.

Our Tech

What sets our platform apart:

• Brand-aware AI that learns your voice, tone, and terminology to ensure every translation is accurate and consistent

• Agentic AI workflows that automate the entire translation process from content ingestion to quality review to publishing

• 100+ native integrations with systems like Adobe Experience Manager, Webflow, Salesforce, GitHub, and Google Drive to simplify content translation

• Human-in-the-loop reviews via our global network of professional linguists, for high-impact content that requires expert review

LILT in the News

• Featured in The Software Report’s Top 100 Software Companies!

• LILT makes it onto the Inc. 5000 List.

• LILT’s continues to be an intellectual powerhouse, holding numerous patents that help power the most efficient and sophisticated AI and language models in the industry.

• Check out all our news on our website.

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LILT is an equal opportunity employer. We extend equal opportunity to all individuals without regard to an individual’s race, religion, color, national origin, ancestry, sex, sexual orientation, gender identity, age, physical or mental disability, medical condition, genetic characteristics, veteran or marital status, pregnancy, or any other classification protected by applicable local, state or federal laws. We are committed to the principles of fair employment and the elimination of all discriminatory practices.