Senior/Staff Machine Learning Engineer, Training Runtime Performance

• 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.

This role accelerates Nuro's self-driving roadmap for commercial and personal mobility by optimizing training runtime efficiency, enabling models to train faster and more efficiently, which contributes to making autonomy accessible to all and empowering a safer, richer, and more connected future.

• 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.

Base pay range is between $235,030 and $352,290, plus an annual performance bonus, equity, and a competitive benefits package. Nuro offers a diverse workplace that fosters inclusion and psychological safety for all employees.

Nuro is a self-driving technology company on a mission to make autonomy accessible to all, building the world's most scalable driver by combining cutting-edge AI with automotive-grade hardware and licensing its core technology to support applications like robotaxis and commercial fleets.