Software Engineer, ML Platform (ML Serving)

Zoox•Foster City, CA

1d•$189,000 - $258,000

About The Position

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 for enabling 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 . In this role, you will: Build the off-vehicle inference service powering our Foundational models (LLMs & VLMs) and the models that improve our rider experiences.

Requirements

4+ years of ML model serving infrastructure experience
Experience building large-scale model serving using GPU and/or high QPS, low latency serving use cases.
Experience with GPU-accelerated inference using RayServe, vLLM, TensorRT, Nvidia Triton, or PyTorch.
Experience working with cloud providers like AWS and working with K8s

Responsibilities

Lead the design, implementation, and operation of a robust and efficient ML serving infrastructure to enable the serving and monitoring of ML models.
Collaborate closely with cross-functional teams, including ML researchers, software engineers, and data engineers, to define requirements and align on architectural decisions.
Enable the junior engineers in the team to grow their careers by providing technical guidance and mentorship