Software Engineer, ML Core

United States·Foster CityFull-timemid

Software EngineerSoftware Engineering

2 views0 saves0 applied

Apply Now

Quick Summary

Overview

Zoox is on a mission to reimagine transportation and ground-up build autonomous robotaxis that are safe, reliable, clean, and enjoyable for everyone.

Technical Tools

cpphuggingfacepythonpytorchdeep-learning

Zoox is on a mission to reimagine transportation and ground-up build autonomous robotaxis that are safe, reliable, clean, and enjoyable for everyone. We are still in the early stages of deploying our robotaxis on public roads, and it is a great time to join Zoox and have a significant impact in executing this mission. The ML Platform team at Zoox plays a crucial role in enabling innovations in ML and AI to make autonomous driving as seamless as possible.

The Opportunity

Would you like to enable ML use cases like autonomous driving, scene understanding, and automated mapping at Zoox? This role works across all ML teams within Zoox - Perception, Behavior ML, Simulation, Data Science, Collision Avoidance, as well as with our Advanced Hardware Engineering group specifying our next generation of autonomous hardware. You will significantly push the boundaries of how ML is practiced within Zoox.

We build and operate the base layer of ML tools, deep learning frameworks, inference libraries, and ML infrastructure used by our applied research teams for in- and off-vehicle ML use cases. We coordinate across all of Zoox to make sure that the needs of both the vehicle and ML teams are met. You will play a crucial role in reducing the time it takes from ideation to productionization of cutting-edge AI innovation. This team has a lot of growth opportunities as we expand our robotaxi deployments and venture into new ML domains. If you want to learn more about our stack behind autonomous driving, please look here.

Design, develop, and deploy custom and off-the-shelf ML libraries and toolings to improve ML development, training, deployment, and on-vehicle model inference latency.

Build tooling and establish development best practices to manage and upgrade foundational libraries, i.e., Nvidia driver, PyTorch, TensorRT, etc., improve ML developer experience, and expedite debugging efforts.

Collaborate closely with cross-functional teams, including applied ML research, high-performance compute, advanced hardware engineering, and data science, to define requirements and align on architectural decisions.

4+ years of experience

Proficient in Python or C++

Familiarity with any of the training frameworks and libraries like PyTorch, Lightning, Hugging Face, Ray, JAX, etc.

Familiarity with any of the GPU-accelerated inference on Nvidia hardware like CUDA, TensorRT, and/or XLA

Familiarity with Bazel and/or the C++ linker