Tech Lead Manager, ML Training Infrastructure
- Setting technical direction for the Training Infrastructure team.
- Staying ahead of emerging research and evaluating new methods.
- Establishing telemetry to root-cause quality regressions in lower precision training.
- Driving the adoption of quantized training methods (e.g., AWQ, AQT, GPTQ) across Nuro’s ML infrastructure to accelerate model training and inference.
- Leading the design and implementation of efficiency initiatives for model training, including low-bit quantization, pruning, and knowledge distillation, for both research and production workloads.
- Collaborating cross-functionally with research, infrastructure, and product teams balancing accuracy, latency, and resource constraints.
- Mentoring and growing a high-performing team of engineers and researchers.
- 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.
- Advanced degree (Ph.D. or strong M.Sc. with research experience) in Computer Science, Electrical Engineering, or related fields.
- Knowledge of sparse networks and complementary model compression techniques (e.g., AdaRound, BRECQ, structured pruning).
- Published work or open-source contributions in quantization methods (e.g., AWQ, AQT, GPTQ, SmoothQuant, ZeroQuant).
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