1 day ago
Belfast, United KingdomStaff+
Responsibilities
- Define architecture and technical roadmaps 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 large-scale LLM inference using techniques including speculative decoding, KV-cache management, continuous batching, quantization, and custom kernels.
- Establish evaluations as release gates for AI systems.
- Lead design reviews, resolve difficult technical trade-offs, and build critical model, training, and inference code.
- Mentor Staff and Senior engineers and influence product and platform investments across the organization.
Requirements
- 12+ 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 used across multiple teams or products and delivering organization-wide impact through technical leadership, mentoring, shared platforms, and standards.
- Deep understanding of transformer architecture, attention, tokenization, positional encodings, model training, and model serving, including the ability to implement core components from scratch.
- Expert-level PyTorch experience with custom model code, training loops, mixed precision, and distributed training.
- Hands-on experience with full and parameter-efficient fine-tuning, distillation, preference or reinforcement-learning-based methods, and open-weight LLMs.
- Experience with 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 defining evaluation strategies and benchmarks for LLM and agent systems.
- Strong predictive AI, classical ML, distributed systems, and Python fundamentals.
- Ability to own significant technical outcomes across multiple teams, communicate technical trade-offs, build cross-functional partnerships, and connect customer and business priorities to technical decisions.
- Preferred: experience pre-training or training LLMs from scratch; writing GPU kernels and profiling GPU workloads; cybersecurity domains such as detection engineering, SOC/MDR, or vulnerability management; running AI workloads on AWS at scale; and AI publications, patents, or open-source contributions.
- 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.
Categories
About Rapid7
Rapid7 builds a cloud security operations platform and services for enterprises, combining vulnerability management, detection and response (SIEM/XDR and MDR), application security testing, and security automation. Its Insight platform products include InsightVM, InsightIDR, InsightAppSec, and InsightConnect, sold as subscriptions with optional managed services. Founded in 2000 and headquartered in Boston, the NASDAQ-listed company (RPD) serves more than 11,500 customers worldwide.