Job Description
Nuro is seeking an ML Performance Engineer to optimize and implement quantization techniques for efficient training and deployment of self-driving vehicle models, collaborating across research and product teams to enhance autonomous driving technology.
Key Responsibilities
- Drive adoption of state-of-the-art quantization techniques for model training and deployment.
- Improve model training efficiency through low-precision training methods, including quantization and pruning.
- Evaluate emerging research and implement new quantization methods for self-driving models.
- Design and implement efficiency initiatives for model training, focusing on low-bit quantization and pruning.
- Identify and troubleshoot model quality and accuracy regressions during low-precision training.
- Collaborate with research, infrastructure, and product teams to optimize accuracy, latency, and resource use.
Requirements
- 3 years of professional or research experience in ML infrastructure, distributed training, or ML systems engineering
- Hands-on experience with quantization methods, including Activation-Aware Weight Quantization AWQ, Accurate Quantized Training AQT, FP-8 training, or related methods
- Experience building or maintaining quantization libraries e.g., AQT, bitsandbytes, NVIDIA Transformer Engine, DeepSpeed Compression
- Understanding of calibration and scaling strategies for quantized models to minimize accuracy loss
Benefits & Perks
Compensation/salary range between $193,930 and $352,290 depending on experience and qualifications
Annual performance bonus
Equity
Competitive benefits package
Ready to Apply?
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