Optimizes ML models for performance on embedded compute platforms in ADAS/AD stacks, focusing on inference efficiency, pruning, quantization, and profiling across GPU/CPU/SoC architectures. Requires 3+ years experience with deep learning frameworks and embedded systems.
159k – 199k/yr
On-site3+ YOEML Engineering
About the role
Responsibilities
Drive ML performance optimization on multiple technologies for on-road and off-road ADAS / AD stacks targeting deployment on a variety of embedded compute platforms
Develop compute usage strategies to optimize efficiency and latency of model inference for compute boards selected by our customers
Work on model pruning and quantization, and support deployment on memory constrained platforms
Collaborate closely with ML engineers and software developers on technical efforts to find and optimize efficient model architecture solutions
Set up methodologies to profile the model performance on target embedded compute platforms and identify performance bottlenecks as part of stack integration
Requirements
Bachelors in Electrical Engineering or Computer Science, OR B.Sc. in Computer Science, Mathematics, Physics or a related field
3+ years of experience with ML accelerators, GPU, CPU, SoC architecture and micro-architecture
Strong software development skills with the focus on embedded programming
Experience profiling and optimizing model performance on embedded compute platforms
Experience in working with deep learning frameworks (e.g., PyTorch, JAX, ONNX, etc.)
Nice to Have
M.Sc or PhD in a ML related area
Built an ML optimization framework from scratch before
Deployed ML solutions to embedded chips for real time robotics applications
Compensation
Base salary range: $159,053 - $199,295 USD annually
Equity, comprehensive health, dental, vision, life and disability insurance, 401k with employer match, learning and wellness stipends, paid time off
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