Principal AI Engineer

🔥 9 hours ago

🏄 California – Remote

🏄 California – Remote

💵 $221k - $299k / year

⏰ Full Time

🔴 Lead

🤖 AI Engineer

🦅 H1B Visa Sponsor

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Rapid7

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2000

🔒 Cybersecurity

Cybersecurity

Rapid7 is a cybersecurity company that provides a range of solutions designed to secure cloud, identity, and endpoint products. The company's Command Platform utilizes artificial intelligence to offer deep insights into cybersecurity threats and vulnerabilities. Rapid7's offerings include Managed XDR for continuous monitoring, detection, and incident response, as well as tools for attack surface management, vulnerability management, and threat intelligence. The company is known for its open-source projects like Metasploit and Velociraptor, which aid in vulnerability research and enhancing cybersecurity frameworks. Rapid7 also emphasizes community and culture, contributing to diversity, equity, and inclusion, and engages in public policy advocacy to strengthen cybersecurity on a broader scale. They serve over 11,000 global companies and deliver expert analysis on attack trends and emergent threats through Rapid7 Labs.

📋 Description

• Define the architecture and technical roadmap for LLM and agentic AI systems across Detection and Response and Exposure and Remediation • Lead model strategy, including 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 using speculative decoding, KV-cache management, continuous batching, quantization, and custom kernels • Establish evaluations as the release gate for every AI system shipped • Lead design reviews and resolve the hardest technical trade-offs • Prototype and build critical model, training, and inference code hands-on • Mentor Staff and Senior engineers • Shape product and platform investment • Help define and influence the technical direction of LLM and agentic AI systems • Raise engineering standards and build greenfield AI solutions for global cybersecurity customers

🎯 Requirements

• 12+ years of software or ML engineering experience • 2+ years building production LLM or agentic AI systems • PhD in Computer Science, Machine Learning, AI or a related field preferred, or equivalent depth of expertise demonstrated through industry experience and technical contributions • Track record of building scalable, production-grade AI systems used across multiple teams or products • Strong history of organization-wide impact through technical leadership, mentoring, and shared platforms and standards • Deep understanding of transformer architecture, attention, tokenization, positional encodings, and model training and serving • Ability to implement core LLM components from scratch • Expert-level PyTorch, including custom model code, training loops, mixed precision, and distributed training such as FSDP and DeepSpeed • Hands-on full and parameter-efficient fine-tuning, including LoRA and QLoRA • Experience with distillation and preference or RL-based methods such as DPO and GRPO • Context engineering, including retrieval, memory, and context management for long-running agent workflows • Deep knowledge of prefill and decode, KV-cache management, continuous batching, speculative decoding, FlashAttention, quantization, and serving at scale • Experience with vLLM, SGLang or TensorRT-LLM • Ability to define evaluation strategies and benchmarks for LLM and agent systems • Strong predictive AI and classical ML fundamentals • Strong distributed systems and Python fundamentals • Ownership of significant technical outcomes across multiple teams • Ability to identify and address risks and drive complex challenges to resolution • Strong communication skills for explaining technical trade-offs to engineering, product, and leadership audiences • Ability to build cross-functional partnerships across Engineering, Product, and other teams • Ability to connect customer needs and business priorities to technical decisions • Experience pre-training or training LLMs from scratch preferred • Experience writing GPU kernels using CUDA or Triton and profiling GPU workloads preferred • Security domain experience preferred • Experience running AI workloads on AWS at scale preferred • Publications, patents, or open-source contributions in AI preferred

🏖️ Benefits

• Variable/incentive compensation (excluded from stated salary range) • Equity (where applicable/eligible) • Benefits (where applicable/eligible)

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