Job Description
Nuro is seeking an ML Performance Engineer to optimize and implement quantization techniques for efficient model training and deployment in autonomous vehicles, collaborating across research and product teams to enhance ML infrastructure.
Key Responsibilities
- Drive adoption of state-of-the-art quantization techniques for model training and deployment.
- Implement and evaluate quantized training methods such as AWQ, AQT, and GPTQ.
- Design and execute efficiency initiatives for model training, including low-bit quantization and pruning.
- Identify and troubleshoot model quality and accuracy regressions related to 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 between $193,930 and $352,290
Annual performance bonus
Equity
Competitive benefits package
Ready to Apply?
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