[Hiring] Principal AI Engineer @Rapid7

Role Description

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.

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

Qualifications

• 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.
• 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.
• Expert-level PyTorch skills, including custom model code, training loops, mixed precision and distributed training (e.g., FSDP, DeepSpeed).
• 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).
• 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.
• 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.

Requirements

• 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.

Benefits

• Competitive salary based on experience and location.
• Comprehensive health benefits.
• Flexible work environment.
• Opportunities for professional development and growth.