Staff Machine Learning Engineer, Training Runtime Performance

Nuro • Mountain View, CA

78d • $235,030 - $352,290

About The Position

Nuro is a self-driving technology company on a mission to make autonomy accessible to all. Founded in 2016, Nuro is building the world’s most scalable driver, combining cutting-edge AI with automotive-grade hardware. Nuro licenses its core technology, the Nuro Driver™, to support a wide range of applications, from robotaxis and commercial fleets to personally owned vehicles. With technology proven over years of self-driving deployments, Nuro gives the automakers and mobility platforms a clear path to AVs at commercial scale—empowering a safer, richer, and more connected future.

Requirements

• B.S./M.S./Ph.D. in Computer Science, Electrical Engineering, or related technical field (or equivalent experience).
• 4+ years of professional experience in ML infrastructure, distributed training, or ML systems engineering, scaling models on multi-node, multi-accelerator clusters.
• Understanding of training, evaluation, and distillation workflows for billion-parameter models.
• Expert-level knowledge in distributed systems and (remote) Python.
• Strong skills in profiling, debugging, and optimizing quantized workloads.
• Experience with ML compilers and strategies to reduce startup overhead.
• Familiarity with model distillation and efficient inference workflows.

Nice To Haves

• Previous contributions to open source ML infra projects or research publications in ML systems.
• Hands-on experience with Foundation Model infrastructure.
• Highly proficient in C++, distributed systems, ML framework internals (e.g., NCCL, Horovod, DeepSpeed, Ray).

Responsibilities

• Collaborate with ML practitioners and other infrastructure teams to understand their needs and integrate optimized input pipelines seamlessly into their workflows.
• Detect, diagnose, and resolve performance bottlenecks across training, eval, and model distillation workflows.
• Optimize training performance, resource utilization, and ensure consistent, reproducible model training outcomes.
• Optimize input data pipelines to increase runtime goodput, ensuring accelerators maximize their 'time on task' and minimize idle cycles.
• Champion best practices for robust, reproducible, and debuggable ML experimentation.

Benefits

• Annual performance bonus
• Equity
• Competitive benefits package

What This Job Offers

• Career Level: Senior
• Education Level: Master's degree
• Number of Employees: 501-1,000 employees