Senior ML Inference Engineer – Platform

Jobtailor; California (MO); On-site; USD 140,000 - 190,000; Full time

Job summary

Jobtailor in the United States is seeking an experienced engineer to design, build, and operate the ML deployment platform that moves trained models to on-vehicle inference.

You will drive cross-organization deployments to the autonomous vehicle stack, partner with model teams, and create tools to diagnose deployment issues and improve observability.

A strong Python background and hands-on experience with production infrastructure are essential to succeed.

Qualifications

• BS, MS, or PhD in Computer Science or a related technical field.
• 3+ years of relevant industry experience.
• Strong Python coding ability.
• 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.

Responsibilities

• Design, build, and operate the ML deployment platform for on-vehicle inference.
• Drive cross-organization 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.

Skills

Python

ML deployment

Model serving

Observability

Production platform

Cross-team collab

Education

BS/MS/PhD in CS

Tools

Cursor

Claude Code

GitHub Copilot

Job description

Responsibilities

• 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.

Requirements

• 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.