Job Description
A Technical Lead Manager role focused on advancing ML infrastructure for autonomous vehicles by driving quantization techniques, optimizing model training and deployment, and leading a team of engineers and researchers in cutting-edge model compression methods.
Key Responsibilities
- Drive adoption of quantization techniques to improve model training and deployment efficiency.
- Set technical strategy for ML training infrastructure and evaluate emerging research methods.
- Lead design and implementation of efficiency initiatives such as low-bit quantization, pruning, and knowledge distillation.
- Collaborate with research, infrastructure, and product teams to optimize accuracy, latency, and resource use.
- Mentor and develop a team of engineers and researchers in ML infrastructure and model compression techniques.
Requirements
- 6 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.
- Knowledge of broader model compression techniques, such as structured unstructured pruning and knowledge distillation.
- 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 $235,030 and $352,290
Annual performance bonus
Equity
Competitive benefits package
Ready to Apply?
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