ML Runtime Optimization Engineer

Job summary

Responsibilities

We are looking for a software engineer with deep experience in optimizing ML models and deploying them on production-grade embedded runtime environments. You’ll work across the entire ML framework stack (e.g. PyTorch, JAX, ONNX, TensorRT, CUDA, XLA, Triton).

• 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

What they're looking for

• 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.)

Tech stack

• PyTorch
• JAX
• CUDA
• Triton