1 day ago
Base Salary
$187k - $252k/yr
Responsibilities
- Lead the architecture and technical roadmap for LLM and agentic AI systems across Detection and Response and Exposure and Remediation.
- Select appropriate model strategies, including frontier versus open-source models, fine-tuning, distillation, and post-training.
- Fine-tune, distill, and post-train open-source LLMs from security-data curation through training, evaluation, and deployment.
- Develop context engineering, inference optimization, and evaluation practices while balancing quality, latency, and cost.
- Establish evaluations and benchmarks as release gates for AI systems.
- Prototype and build critical model, training, and inference code hands-on.
- Lead design reviews, resolve complex technical trade-offs, mentor engineers, and partner with Product and platform teams.
- Translate customer needs into scalable, commercially viable AI solutions and lead projects of moderate scope and risk.
Requirements
- 8+ years of software or ML engineering experience, including 2+ years building production LLM or agentic AI systems.
- Track record of delivering scalable, production-grade AI systems used by multiple teams or products, with technical leadership and mentoring impact.
- Deep understanding of transformer architecture, attention, tokenization, positional encodings, and model training and serving.
- Advanced PyTorch skills, including custom model code, training loops, mixed precision, and distributed training with technologies such as FSDP and DeepSpeed.
- Hands-on experience fine-tuning and post-training open-weight LLMs using LoRA, QLoRA, distillation, DPO, and GRPO.
- Experience with context engineering, retrieval, memory, long-running agent workflows, and evaluation strategies for LLM and agent systems.
- Knowledge of inference optimization, including prefill and decode, KV-cache management, continuous batching, speculative decoding, quantization, and serving with vLLM, SGLang, or TensorRT-LLM.
- Strong predictive AI, classical ML, distributed systems, and Python fundamentals, with judgment to select approaches based on accuracy, latency, and cost.
- Ability to lead AI projects, manage risks and dependencies, drive technical outcomes across teams, and communicate complex trade-offs to technical and non-technical audiences.
- Ability to build cross-functional partnerships and translate customer needs into scalable AI solutions.
- A Master’s degree in Computer Science, Machine Learning, AI, or a related field is preferred, or equivalent practical experience.
Benefits
- Remote work is indicated by the #LI-Remote designation.
- Annual U.S. salary range is $186,500.00–$252,300.00 depending on location, excluding variable compensation, equity, and benefits.
- Rapid7 supports a collaborative, inclusive workplace focused on cybersecurity innovation and professional impact.
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.