Senior Principal AI Engineer

Rapid7•Remote - California, CA

1d•$238,200 - $322,200•Remote

About The Position

Rapid7 is seeking Senior Principal AI Engineers to define and influence the technical direction of LLM and agentic AI systems across the organization. The role addresses complex technical challenges, establishes architectural principles and engineering standards, drives greenfield innovation, and shapes strategic product and platform investments through technical leadership and mentoring. Rapid7’s AI Engineering team builds production AI for its security platform, combining open-source and frontier LLMs, AI agents, predictive models, and the company’s security expertise. The team supports Detection and Response use cases such as alert triage, threat investigation, and automated response, as well as Exposure and Remediation use cases such as vulnerability prioritization, attack surface risk assessment, and guided remediation. The Senior Principal AI Engineer sets technical direction for building, tuning, and serving LLMs and agentic systems across security products. The role requires understanding models from transformer internals and training through fine-tuning and inference optimization, while selecting among LLMs, predictive AI, and classical ML according to the problem. The position leads cross-team initiatives, sets company-wide standards, mentors senior technical colleagues, and represents AI Engineering to executives, customers, and the external community.

Requirements

14+ years of software or ML engineering experience, including 2+ years building production LLM or agentic AI systems.
Track record of building scalable, production-grade AI systems and shared platforms adopted across multiple products, with company-wide impact through technical leadership, mentoring, and standards.
Leading-edge expertise in LLMs, including transformer architecture, attention, tokenization, positional encodings, and model training and serving.
Advanced PyTorch skills, including custom model code, 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.
Deep 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.
Deep knowledge of inference optimization, including prefill and decode, KV-cache management, continuous batching, speculative decoding, and quantization.
Experience serving models 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.
Accountability for organization-wide technical outcomes, including proactively addressing systemic risks and removing delivery barriers while aligning technical decisions with strategic priorities.
Ability to articulate complex architectural trade-offs, build consensus, and influence technical and product decisions across the organization.
Ability to thrive in ambiguity, drive change, and adapt as AI technology and the threat landscape evolve.
Ability to embody Rapid7’s core values and foster a culture of excellence, meaningful impact, and collective success.

Nice To Haves

Ph.D. in Computer Science, Machine Learning, AI, or a related field, or equivalent depth of expertise demonstrated through industry experience and technical contributions.
Experience training LLMs from scratch.
Inference-serving experience on AWS and GPU kernel work using CUDA or Triton.
Recognition as an AI expert evidenced by publications, patents, open-source contributions, or industry engagement.

Responsibilities

Set the AI technical vision, architecture, and multi-year roadmap for LLM and agentic systems across Detection and Response and Exposure and Remediation.
Define company-wide model strategy, including the use of frontier or open-source models, fine-tuning, distillation, post-training, and investment in new capabilities.
Lead complex, high-risk technical initiatives across multiple teams, including fine-tuning, distillation, and post-training open-source LLMs on security data.
Establish company-wide standards for context engineering, inference optimization, and evaluation, with evaluations serving as a release gate for every AI system shipped.
Lead optimization of LLM inference at scale, balancing quality, latency, and cost.
Lead design reviews, resolve complex technical trade-offs, and remain hands-on by prototyping and building critical model, training, and inference code.
Mentor and develop Principal, Staff, and Senior engineers, strengthening AI engineering talent across Rapid7.
Represent Rapid7’s AI Engineering to executives, customers, and the external community, influencing product, platform, and business strategy.
Build alignment across Engineering, Product, and other functions on technical direction and decisions that shape product and platform investments.
Anticipate customer needs and guide AI architecture and technical strategy to maximize customer value, balancing capability, performance, cost, and scalability.