Software Engineer, ML Inference Optimization

Overview

Zoox is on a mission to reimagine transportation and ground-up build autonomous robotaxis that are safe, reliable, clean, and enjoyable for everyone. The ML Platform team at Zoox plays a crucial role in enabling innovations in large-scale Foundation models, VLMs, and VLAs to make autonomous driving as seamless as possible.

The Opportunity

Drive ML Inference Optimization initiatives to make ML models that enable autonomous driving as fast and efficient as possible. Work with SOTA accelerators, cutting-edge techniques in distributed training, quantization, distillation, and pruning, collaborating closely with Autonomy teams across Zoox (Perception, Prediction, Planner, Simulation, Collision Avoidance).

Responsibilities

• Design, implement, and operate cutting-edge ML Training OR Inference performance optimization techniques to scale VLM, VLA, and Foundational models and deploy them efficiently in the robotaxi.
• Collaborate closely with cross-functional teams, including ML researchers, software engineers, data engineers, and hardware engineers, to define requirements and align on architectural decisions.

Qualifications

• 4+ years of total experience, including 2+ years working on large-scale model training or inference platforms.
• Experience with training frameworks like PyTorch, leveraging GPUs efficiently for distributed model training.
• Experience with GPU-accelerated inference using TensorRT or similar frameworks.
• Experience using profiling tools like NVIDIA's Nsight or PyTorch's Profiler for identifying model training and serving bottlenecks.
• Proficient in Python or C++.