Job Description
Nuro is seeking an ML Performance Engineer to optimize and implement quantization techniques for efficient training and deployment of autonomous vehicle models, collaborating across research and product teams to enhance model performance and resource utilization.
Key Responsibilities
- Drive adoption of state-of-the-art quantization techniques for efficient model training and deployment.
- Implement quantized training methods such as AWQ, AQT, and GPTQ for self-driving models.
- Design and execute efficiency initiatives for model training, including low-bit quantization and pruning.
- Identify and address root causes of 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 such as AQT, bitsandbytes, NVIDIA Transformer Engine, or DeepSpeed Compression
- Understanding of calibration and scaling strategies for quantized models to minimize accuracy loss
Benefits & Perks
Compensation/salary range: $193,930 to $352,290 (base pay)
Annual performance bonus
Equity
Competitive benefits package
Ready to Apply?
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