Staff Machine Learning Operations Engineer - Computer Vision

About the role

Automated Tire (ATI) is a Series-B startup revolutionizing automotive service with innovative robotic and software technology. Founded by experienced entrepreneurs and backed by major players in the automotive and tire sectors, ATI is building the next generation of tools that make tire shops and dealership service lanes faster, safer, and smarter. If you're passionate about building products that ship into real-world environments, ATI is the place for you.

BrakeWise is our production brake inspection product: a mobile application paired with a camera probe that technicians use to assess pad and rotor condition during live service work. The machine learning behind it is a multi-stage pipeline of segmentation and classification models that turn raw imagery into a wear assessment a shop can act on and charge for. That pipeline works, and it is an MVP. It runs on Cloud Functions, and it will not carry us to the customer volume we're signing. We're looking for a Staff MLOps Engineer to own it — to take it from a working prototype to a serving architecture that holds up under real throughput, with the latency, cost, and reliability characteristics a paying customer expects. You'll own every aspect of how our models reach production and how they get better: serving infrastructure, deployment and rollback, monitoring and drift detection, the retraining loop, and the evaluation discipline that tells us whether a new model is actually an improvement. Model accuracy here has commercial consequences — a bad wear call is either a missed repair or an unnecessary one, in front of a customer. This is also the senior cloud architecture voice on the team. You'll partner closely with our Staff Full Stack Engineer, who owns the mobile app and customer dashboard, reviewing designs and setting GCP practices across the platform rather than only within the ML stack.

Responsibilities

• Own the multi-stage inference pipeline (segmentors and classifiers) end to end — serving architecture, latency, throughput, reliability, and cost per inspection
• Re-architect the pipeline off its current Cloud Functions MVP onto infrastructure that scales: containerized inference, GPU-backed or accelerated serving where it pays for itself, queueing, batching, and autoscaling
• Own model deployment: versioning, staged rollout, canary and shadow evaluation, and fast rollback when a model regresses
• Build and own the improvement loop — field data collection, labeling workflows, dataset versioning, evaluation harnesses, and regression suites that catch quality loss before customers do