Job Description
The role involves optimizing machine learning infrastructure for self-driving technology, focusing on improving training efficiency, input pipelines, and system performance to accelerate autonomous vehicle development.
Key Responsibilities
- Collaborate with ML practitioners and infrastructure teams to integrate optimized input pipelines into workflows
- Detect, diagnose, and resolve performance bottlenecks in training, evaluation, and model distillation workflows
- Optimize training performance, resource utilization, and ensure reproducible model training outcomes
- Enhance input data pipelines to increase runtime efficiency and maximize accelerator utilization
- Champion best practices for robust, reproducible, and debuggable ML experimentation
Requirements
- A B.S., M.S., or Ph.D. in Computer Science, Electrical Engineering, or related technical field or equivalent experience.
- At least 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.
- Hands-on experience with Foundation Model infrastructure.
- Highly proficient in C++, distributed systems, and ML framework internals such as NCCL, Horovod, DeepSpeed, or Ray.
Benefits & Perks
Compensation/salary range between $235,030 and $352,290 depending on experience and qualifications
Annual performance bonus
Equity
Competitive benefits package
Ready to Apply?
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