ML Research Engineer, Performance Optimization

Highlights

• Maximize iteration speed for mid- and post-training on 1T+ parameter LLMs.
• Own GPU, cluster and RL pipeline performance for frontier-scale research.
• Define throughput-accuracy tradeoffs guiding the AI research roadmap.

What they offer

• Competitive base compensation with bonus potential
• Generous early-stage equity
• Medical, dental and vision coverage
• Employer-paid life and disability insurance
• Flexible time off with company-wide holidays
• Paid parental leave
• Educational assistance program
• Commuter benefits
• Company-subsidized lunch program

Ideal candidate

A performance-obsessed ML engineer who loves squeezing maximum speed from large-scale training and building reusable, well-tested platforms that accelerate an entire research team.

Requirements

• BS/MS in Computer Science or related quantitative field and 4+ years as software or ML engineer, or PhD and 1+ years work experience
• Experience with LLM inference libraries such as vLLM and training libraries such as Megatron including throughput optimization
• Experience running LLM inference and/or training at scale across multi-node GPU clusters
• Familiarity with core concepts in reinforcement learning
• Proven track record of software engineering excellence with clean APIs and well-tested code
• Hands-on experience supporting and maintaining production libraries used by engineers and researchers
• Strong CI/CD skills designing integration tests, build pipelines and quality gates
• Passion for agentic systems and cutting-edge LLM tool use and autonomy