Staff AI Engineer

Rapid7•Remote - California, CA

1d•$186,500 - $252,300•Remote

About The Position

Rapid7 is scaling its AI Engineering capabilities to build sophisticated LLM and agentic AI systems for its security products. The Staff AI Engineer owns technical solutions end to end, shapes technical direction and roadmaps, and helps translate customer needs into scalable AI solutions across Detection and Response and Exposure and Remediation. The role combines hands-on model and systems engineering with cross-team technical leadership, architectural guidance, design reviews, and mentoring. The AI Engineering team takes models from research to production, combining open-source and frontier LLMs, AI agents, predictive models, and Rapid7’s security expertise. Its products support use cases including alert triage, threat investigation, automated response, vulnerability prioritization, attack surface risk assessment, and guided remediation. The role includes selecting appropriate model strategies; fine-tuning, distilling, and post-training open-source LLMs; improving context engineering, inference optimization, and evaluation; and establishing evaluations as release gates. The engineer partners with Product and platform teams to align technical solutions with customer needs and balance value, performance, and operating costs.

Requirements

At least 8 years of software or ML engineering experience, including at least 2 years building production LLM or agentic AI systems.
Track record of leading delivery of scalable, production-grade AI systems used by multiple teams or products, with organizational impact through technical leadership and mentoring.
Deep understanding of LLMs, including transformer architecture, attention, tokenization, positional encodings, and how models are trained and served.
Advanced PyTorch skills, including custom model code, training loops, mixed precision, and distributed training such as FSDP or DeepSpeed.
Hands-on experience fine-tuning and post-training open-weight LLMs using full and parameter-efficient methods such as LoRA and QLoRA, distillation, and preference- or RL-based methods such as DPO and GRPO.
Experience with context engineering, including retrieval, memory, and context management for long-running agent workflows, and with defining evaluation strategies and benchmarks for LLM and agent systems.
Strong knowledge of inference optimization, including prefill and decode, KV-cache management, continuous batching, speculative decoding, quantization, and serving at scale with vLLM, SGLang, or TensorRT-LLM.
Strong predictive AI, classical ML, distributed systems, and Python fundamentals, with judgment to choose approaches based on accuracy, latency, and cost.
Ability to lead AI projects or programs of moderate scope and risk, contribute to execution strategy, break larger goals into milestones, and hold oneself and others to clear expectations and commitments.
Ability to own complex technical outcomes across teams, manage risks and dependencies proactively, and drive issues to resolution.
Strong communication skills, including explaining technical concepts and trade-offs to technical and non-technical audiences and building alignment.
Ability to build cross-functional partnerships and deliver shared outcomes.
Ability to translate customer needs into commercially viable, scalable AI solutions.
Commitment to embodying Rapid7’s core values.

Nice To Haves

A Master’s degree in Computer Science, Machine Learning, AI, or a related field; equivalent practical experience is also accepted.

Responsibilities

Lead the design and architecture of LLM and agentic AI systems in Detection and Response and Exposure and Remediation, contributing to the technical roadmap.
Recommend model strategies, including whether to use frontier or open-source models and whether to fine-tune, distill, or post-train them.
Fine-tune, distill, and post-train open-source LLMs on security data, from data curation through training, evaluation, and deployment.
Apply and improve engineering practices for context engineering, inference optimization, and evaluation, balancing quality, latency, and cost.
Establish evaluations as release gates for AI systems.
Lead design reviews and work through complex technical trade-offs.
Prototype and build critical model, training, and inference code.
Mentor engineers and partner with Product and platform teams to align technical solutions with customer needs.
Lead AI projects or programs of moderate scope and risk, contributing to execution strategy, breaking larger goals into milestones, and setting clear expectations and commitments.
Own complex technical outcomes across teams, proactively manage risks and dependencies, follow through on commitments, and drive issues to resolution.
Communicate complex technical concepts and trade-offs to technical and non-technical audiences and build alignment around engineering decisions.
Build cross-functional partnerships across Engineering, Product, and other teams to resolve dependencies, align technical approaches, and deliver shared outcomes.
Translate customer needs into scalable AI solutions, balancing customer value, performance, and development and operating costs.
Embody Rapid7’s core values and contribute to a culture of excellence, meaningful impact, and collective success.