Design, build, and operate the ML deployment platform that automates the path from trained model to on-vehicle inference.
Drive cross-organization model deployments to the autonomous vehicle stack, partnering with model development teams to take high-value models from training to production on-vehicle.
Build agentic tools that diagnose and fix deployment-blocking issues, automating workflows currently performed manually by engineers.
Build the developer experience that ML model development teams use day to day: tooling, dashboards, automation, and observability.
Drive shift-left validation that surfaces deployment risk (compile, runtime, parity, latency) early in the model development cycle.
Build platform tools that integrate the work of our sister teams (kernels, compiler, reduced precision and parity) so their optimization wins land directly in the deployment workflow.
Partner with the team's Performance pillar and model development teams across the AV organization.

BS, MS, or PhD in Computer Science or a related technical field.
3+ years of relevant industry experience.
Strong fundamentals and excellent coding ability in Python.
Experience building or operating production platform or infrastructure systems where reliability, observability, and extensibility matter.
Experience with ML model deployment, inference integration, model optimization workflows, or model serving infrastructure, with at least one prior context where you owned the path from a trained model to a running inference workload.
Experience using coding agents (Cursor, Claude Code, GitHub Copilot, or equivalent) as part of your engineering workflow.
Experience designing clean, well-tested software with clear interfaces and good abstractions.
Strong cross-team collaboration skills.

Senior ML Inference Engineer – Platform

Key skills