1 day ago
Dublin, IrelandStaff+
Responsibilities
- Define architecture and technical roadmaps for LLM and agentic AI systems across security products.
- Set 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 from security-data curation through training, evaluation, and deployment.
- Establish 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.
- Make evaluations release gates for AI systems and define benchmarks for LLM and agent systems.
- Lead design reviews, resolve difficult technical trade-offs, and build critical model, training, and inference code hands-on.
- Mentor Staff and Senior engineers and shape 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.
- Expert-level PyTorch experience, including custom model code, training loops, mixed precision, and distributed training with technologies such as FSDP and DeepSpeed.
- Deep understanding of transformer architecture, attention, tokenization, positional encodings, and model training and serving, with the ability to implement core components from scratch.
- Hands-on experience with full and parameter-efficient fine-tuning, LoRA, QLoRA, distillation, and preference or reinforcement-learning methods such as DPO and GRPO.
- Experience with context engineering, retrieval, memory, and context management for long-running agent workflows.
- Deep knowledge of prefill and decode, KV-cache management, PagedAttention, prefix caching, continuous batching, speculative decoding, FlashAttention, quantization, and large-scale model serving.
- Strong predictive AI, classical ML, distributed systems, and Python fundamentals.
- Demonstrated experience building scalable production AI systems used across multiple teams or products and delivering organization-wide technical impact.
- Strong technical leadership, communication, cross-functional collaboration, ownership, and mentoring skills.
- Preferred: experience pre-training or training LLMs from scratch; writing GPU kernels with CUDA or Triton; profiling GPU workloads; running AI workloads on AWS at scale; security-domain experience; or AI publications, patents, or open-source contributions.
Benefits
- Annual salary range varies by location; compensation excludes variable or incentive compensation, equity, and benefits where applicable or eligible.
- Rapid7 is committed to fair and equitable compensation practices and evaluates salary based on factors including experience, skills, and certifications.
- The role supports work on cybersecurity products serving more than 11,500 customers.
- Rapid7 provides an inclusive workplace and equal employment consideration for protected characteristics.
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.