3 days ago
Base Salary
$221k - $299k/yr
Responsibilities
- Define architecture and technical roadmaps for LLM and agentic AI systems across Detection and Response and Exposure and Remediation.
- Lead model strategy, including decisions about frontier versus open-source models, fine-tuning, distillation, and post-training.
- Fine-tune, distill, and post-train open-source LLMs using security data from curation through deployment.
- Set engineering standards for context engineering, inference optimization, and AI evaluation.
- Optimize large-scale LLM inference using speculative decoding, KV-cache management, continuous batching, quantization, and custom kernels.
- Establish evaluation gates for every AI system released.
- Lead design reviews, resolve complex 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 with organization-wide technical impact.
- Deep understanding of transformer architecture, attention, tokenization, positional encodings, and model training and serving, including the ability to implement core components from scratch.
- Expert-level PyTorch experience, including custom model code, training loops, mixed precision, FSDP, and DeepSpeed.
- 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 retrieval, memory, and context management for long-running agent workflows.
- Deep knowledge of inference optimization, including prefill and decode, KV-cache management, PagedAttention, prefix caching, 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 teams, communicate technical trade-offs, and connect customer and business priorities to AI investment decisions.
- Preferred: experience pre-training or training LLMs from scratch, writing CUDA or Triton GPU kernels, profiling GPU workloads, running AI workloads on AWS at scale, or contributing publications, patents, or open-source work in AI.
- Preferred: cybersecurity experience in areas such as detection engineering, SOC/MDR, or vulnerability management.
Benefits
- Remote work is indicated by the #LI-Remote designation.
- The stated U.S. annual base salary range is $221,000.00-$299,000.00, excluding variable compensation, equity, and benefits.
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.