# ML Research Engineer, Performance Optimization at Lila Sciences

- Company: Lila Sciences
- Status: Open
- Workplace: On-site
- Location: Cambridge, MA USA; San Francisco, CA USA
- Level: Mid-level
- Discipline: AI & ML
- Employment: Full-time
- Salary: $148K–240K
- Posted: 2026-11-10
- Skills: ML, NVIDIA, GPU
- Apply: https://job-boards.greenhouse.io/lilasciences/jobs/4455701009

## Description

Your Impact at LILA 
 Lila is building a platform where AI and automation co-evolve to accelerate scientific discovery Our AI Research team is looking to hire a ML Research Engineer to maximize iteration speed for mid- and post-training runs on 1T+ parameter LLMs using the latest NVIDIA chips. This involves not only maximizing GPU utilization on an individual training run but also making these optimizations maintainable, reusable, and adaptable so that the rest of the research team always knows how to confidently use the latest optimized stack.
 What You'll Be Building 
• GPU compute performance: profiling, kernel development, long-context handling, memory footprint optimization
• GPU cluster performance: collective/MoE framework optimization, comm/compute overlap, RDMA/IB/EFA usage, P/D disaggregation
• Big data handling: manage mid-training scale data across storage, streaming, tokenization, and processing
• RL pipeline: optimize the async RL cycle, including environment/sandbox interfacing, long rollout handling, large token metadata (such as router replay), and weight sync
• Setting the performance standard: make sure optimized configurations are tested, accessible, and adaptable to research tasks so that the whole team can work at maximum velocity
• Establishing the Pareto frontier: communicate with researchers about throughput/accuracy tradeoffs (e.g. lower-precision compute, higher async staleness) to establish optimal points
 What You'll Need to Succeed 
• BS/MS in Computer Science or a related quantitative field and 4+ years working as a software engineer or machin

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