Drive ML performance optimization on ADAS/AD stacks for embedded compute platforms.
Develop compute usage strategies to optimize inference efficiency and latency.
Work on model pruning and quantization for memory-constrained platforms.
Collaborate with ML engineers and software developers on efficient model architectures.
Set up methodologies to profile model performance on target embedded platforms.

🎯 Requirements

Bachelors in Electrical Engineering or Computer Science, or related field.
3+ years with ML accelerators, GPU/CPU/SoC architecture.
Strong software development skills with embedded programming.
Experience profiling and optimizing model performance on embedded platforms.
Experience with deep learning frameworks (PyTorch, JAX, ONNX, etc.).
MSc or PhD in ML-related area (nice to have).

🎁 Benefits

Equity and benefits package included.
Health, dental, vision, life and disability insurance.
401k with employer match.
Paid time off and wellness stipends.

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