Principal AI Engineer

Our Engineering organisation is scaling its AI Engineering capabilities under new leadership, creating an opportunity to build the next generation of AI at Rapid7. We’re looking for Principal AI Engineers to join us and help shape what comes next by defining and influencing the technical direction of our LLM and agentic AI systems. As a Principal AI Engineer, you’ll raise the bar for engineering standards, and build truly cutting edge greenfield solutions that deliver real impact for our Global customers. Through your technical leadership and mentoring, you’ll help shape our product and platform investments and influence how we build and scale AI across the organisation.

About the Team
AI is reshaping cybersecurity on both sides. Attackers can use AI to strike at machine speed, and only AI-powered defense can keep up. At the same time, organizations can only adopt AI with confidence if it is secure. Rapid7 sits at the intersection of security and AI, and we are building industry-defining products for this new era.

Rapid7's AI Engineering team builds the AI that powers our security platform. We are AI engineers and applied scientists who take models from research to production, combining open-source and frontier LLMs, AI agents, and fast predictive models with more than 20 years of Rapid7 security expertise. Our work drives core use cases across Detection and Response, such as alert triage, threat investigation, and automated response, and Exposure and Remediation, such as vulnerability prioritization, attack surface risk assessment, and guided remediation. Together, these help customers stop threats faster and fix what matters most.

About the Role

As a Principal AI Engineer, you will set the technical direction for how Rapid7 builds, tunes and serves LLMs and agentic systems across its security products. You understand LLMs from the ground up, from transformer internals and training to fine-tuning and inference optimization, and you stay hands-on with the hardest problems. You know where LLMs shine and where they fall short, and you treat them as one tool in the AI toolbox, alongside predictive AI and classical ML, choosing the right approach for each problem. Specifically your focus will be to:

• Define the architecture and technical roadmap for LLM and agentic AI systems across Detection and Response and Exposure and Remediation
• Lead model strategy: when to use frontier or open-source models, and when to fine-tune, distill, or post-train
•  Fine-tune, distill and post-train open-source LLMs on security data, from data curation through training, evaluation and deployment
• Set engineering standards for context engineering, inference optimization, and evaluation across teams
• Optimize LLM inference at scale with techniques such as speculative decoding, KV-cache management, continuous batching, quantization and custom kernels, balancing quality, latency and cost
•  Establish evals as the release gate for every AI system we ship
• Lead design reviews and resolve the hardest technical trade-offs
• Stay hands-on: prototype and build critical model, training and inference code yourself
• Mentor Staff and Senior engineers and shape product and platform investment

The skills and qualities you’ll bring include:

• 12+ years of software or ML engineering experience, including 2+ years building production LLM or agentic AI systems. A PhD in Computer Science, Machine Learning, AI or a related field is preferred, or equivalent depth of expertise demonstrated through industry experience and technical contributions
• A track record of building scalable, production-grade AI systems used across multiple teams or products with a strong history of organization-wide impact through technical leadership, mentoring, and shared platforms and standards
•  LLMs from first principles: deep understanding of transformer architecture, attention, tokenization and positional encodings, and how models are trained and served, with the ability to implement core components from scratch
• PyTorch: expert-level, including custom model code, training loops, mixed precision and distributed training (e.g., FSDP, DeepSpeed)
• Fine-tuning and post-training: hands-on full and parameter-efficient fine-tuning (LoRA, QLoRA) of open-weight LLMs, distillation, and preference or RL-based methods (e.g., DPO, GRPO)
•  Context engineering: retrieval, memory, and context management for long-running agent workflows
• Inference optimization: deep knowledge of prefill and decode, KV-cache management (e.g., PagedAttention, prefix caching), continuous batching, speculative decoding, FlashAttention and quantization, and serving at scale with vLLM, SGLang or TensorRT-LLM
•  Evals: defining evaluation strategy and benchmarks for LLM and agent systems
• Strong predictive AI and classical ML fundamentals, and the judgment to choose between them and LLMs based on accuracy, latency and cost
• Strong distributed systems and Python fundamentals
• Takes ownership of significant technical outcomes across multiple teams, proactively identifying and addressing risks, driving complex challenges to resolution and ensuring solutions deliver against agreed technical and customer objectives
• Strong communicator with the ability to explain technical trade-offs to engineering, product, and leadership audiences
• Builds strong cross functional partnerships across Engineering, Product, and other teams to align on technical direction and deliver shared outcomes
• Connects customer needs and business priorities to technical decisions, evaluating the value, cost and trade-offs of AI solutions to ensure investments deliver measurable customer and business outcomes
• Embody our core values to foster a culture of excellence that drives meaningful impact and collective success

Preferred Skills and Experience:

• Experience pre-training or training LLMs from scratch
• Experience writing GPU kernels (CUDA or Triton) and profiling GPU workloads
• Security domain experience, such as detection engineering, SOC/MDR, or vulnerability management
• Experience running AI workloads on AWS at scale
• Publications, patents, or open-source contributions in AI

We know that the best ideas and solutions come from multi-dimensional teams. That’s because these teams reflect a variety of backgrounds and professional experiences. If you are excited about this role and feel your experience can make an impact, please don’t be shy - apply today.


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About Rapid7

At Rapid7, our vision is to create a secure digital world for our customers, our industry, and our communities. We do this by harnessing our collective expertise and passion to challenge what’s possible and drive extraordinary impact. We’re building a dynamic and collaborative workplace where new ideas are welcome.

Protecting 11,500+ customers against bad actors and threats means we’re continuing to push the envelope just like we’ ve been doing for the past 20 years. If you ’re ready to solve some of the toughest challenges in cybersecurity, we’re ready to help you take command of your career. Join us.

Rapid7, Inc. is committed to fair and equitable compensation practices. A candidate’s salary is determined by various factors including, but not limited to, relevant work experience, skills, and certifications. We evaluate compensation decisions on a case-by-case basis, and it is not typical for an individual to be hired at the very top of the salary range.

The annual salary range for this role, depending on location, is:


United States: $221,000.00 - 299,000.00 USD Annual

Salary ranges may vary based on geographical location. This range does not include variable/incentive compensation, equity and benefits (where applicable/eligible).

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or any other status protected by applicable national, federal, state or local law.