ML Runtime Optimization Engineer
Core
Optimizing ML models and deploying them on production-grade embedded runtime environments for ADAS/AD stacks.
Role type
Senior IC ML Runtime Optimization Engineer
Builds
Efficient model inference on embedded compute platforms for automotive and defense ADAS/AD systems
Domain
Automotive/Defense embedded systems and AI inference
Deliverable
production ML models
Required skills
ML model optimization, embedded programming, model pruning, quantization, performance profiling, deep learning frameworks, GPU/CPU/SoC architecture
Preferred skills
ML optimization framework development, real-time robotics deployment, M.Sc/PhD in ML
Technologies
PyTorch, JAX, ONNX, TensorRT, CUDA, XLA, Triton
Responsibilities
Drive ML performance optimization for on-road and off-road ADAS/AD stacks; Develop compute usage strategies to optimize efficiency and latency; Work on model pruning and quantization for memory constrained platforms; Collaborate with ML engineers and software developers to optimize model architecture; Set up methodologies to profile model performance and identify bottlenecks on target embedded platforms
Seniority
Senior, hands-on IC
