ML Runtime Optimization Engineer

What you'll do

• Optimize ML performance for ADAS and autonomy deployments across embedded compute platforms.
• Develop strategies to improve inference efficiency and latency, including pruning and quantization for memory-constrained platforms.
• Profile models on target platforms, identify bottlenecks, and collaborate on efficient model architectures and stack integration.

What they ask for

• Bachelor’s degree 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 focused on embedded programming.
• Experience profiling and optimizing models on embedded compute platforms and working with deep-learning frameworks.

Nice to have

• M.Sc. or PhD in an ML-related area; built an ML optimization framework from scratch.
• Deployed ML solutions to embedded chips for real-time robotics applications.

Tools

PyTorch, JAX, ONNX, TensorRT, CUDA, XLA, Triton

AI focus

Optimizes, prunes, and quantizes ML models for efficient, low-latency deployment on embedded compute platforms.