Principal AI Engineer
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Job Description
Principal AI Engineer defining LLM and agentic AI architecture for Rapid7’s cybersecurity platform. Building scalable models, inference systems, and evaluation standards for security products.
Responsibilities:
- Define the architecture and technical roadmap for LLM and agentic AI systems across Detection and Response and Exposure and Remediation
- Lead model strategy, including selection of frontier or open-source models and decisions about fine-tuning, distillation and post-training
- Fine-tune, distill and post-train open-source LLMs on security data from 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 complex technical trade-offs
- Prototype and build critical model, training and inference code hands-on
- Mentor Staff and Senior engineers
- Shape product and platform investment
- Build AI systems powering cybersecurity use cases including alert triage, threat investigation, automated response, vulnerability prioritization, attack surface risk assessment and guided remediation
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
- Deep understanding of transformer architecture, attention, tokenization, positional encodings, 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 involving retrieval, memory and context management for long-running agent workflows
- Deep knowledge of inference optimization, including prefill and decode, KV-cache management, continuous batching, speculative decoding, FlashAttention and quantization
- Experience serving AI workloads at scale 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
- Strong communication skills for explaining technical trade-offs
- Ability to build cross-functional partnerships
- Ability to connect customer needs and business priorities to technical decisions
- Ability to embody the company’s core values
- Preferred: 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 such as detection engineering, SOC/MDR or vulnerability management
- Preferred: experience running AI workloads on AWS at scale
- Preferred: publications, patents or open-source AI contributions
Benefits:
- Flexible/remote work arrangement indicated by the location options and #LI-Remote
- Mentoring and technical leadership opportunities
- Opportunity to shape product and platform investments
- Collaborative, multi-dimensional team environment
- Career development opportunity through challenging cybersecurity and AI work
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