Nuro is seeking a Staff Software Engineer to optimize training runtime efficiency and input pipelines for ML model training, evaluation, and distillation workloads, accelerating the company's self-driving roadmap.
Requirements
- 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.
- 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.
Other
- B.S./M.S./Ph.D. in Computer Science, Electrical Engineering, or related technical field (or equivalent experience).
- Previous contributions to open source ML infra projects or research publications in ML systems.
- Hands-on experience with Foundation Model infrastructure
- At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees.
- Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics.
