Senior Inference Optimization ML Engineer

Highlights

• Own cost-efficient inference end-to-end for generalist intelligent robots.
• Decide how work splits between robot and cloud for real-time control.
• Deliver gains that lower serving cost and put capable models on more robots.

Ideal candidate

A pragmatic, high-ownership ML systems engineer with a few years optimizing inference who cares about total cost and real-world performance and thrives turning vague slow-or-expensive problems into validated speedups.

Requirements

• 2+ years of experience in inference optimization, ML systems, ML compilers, or a closely related field
• Deep hands-on experience with PyTorch
• Strong understanding of compute, memory bandwidth, memory capacity, and interconnect bottlenecks across hardware architectures
• Experience with model optimization techniques including quantization, distillation, pruning, and compilation
• High-ownership, pragmatic mindset focused on total solution cost and turning vague performance issues into validated improvements