Staff Machine Learning Operations Engineer - Computer Vision

ATI•Woburn, MA

8d•Onsite

About The Position

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. The BrakeWise product is a production brake inspection system that uses a mobile application paired with a camera probe to assess pad and rotor condition. The machine learning pipeline, consisting of segmentation and classification models, converts raw imagery into a wear assessment. Currently, this pipeline runs on Cloud Functions as an MVP and needs to be scaled to handle customer volume. This role will own the entire ML pipeline, from serving infrastructure and deployment to monitoring, retraining, and evaluation, ensuring it meets the latency, cost, and reliability expectations of paying customers. Model accuracy is critical, as incorrect wear calls have significant commercial implications. This position also serves as the senior cloud architecture voice on the team, collaborating with the Staff Full Stack Engineer to review designs and establish GCP practices across the platform.

Requirements

8+ years of professional engineering experience, including several years owning machine learning systems in production — not solely model development
Demonstrated experience taking a computer vision pipeline from prototype to production scale, serving real users at meaningful volume
Deep experience deploying and operating segmentation and classification models, including multi-stage pipelines where one model’s output feeds the next
Strong cloud infrastructure background, preferably GCP — Vertex AI, Cloud Run, GKE, Cloud Functions, Cloud SQL, Pub/Sub, and Docker
Production-grade Python, and fluency with PyTorch or TensorFlow
Hands-on experience with model serving and optimization — Triton, TorchServe, ONNX, TensorRT, quantization, or equivalent
Experience owning deployment and support for a live system, including incident response, rollback, and on-call
Experience building data and labeling pipelines with dataset versioning and reproducible evaluation
Comfort with infrastructure as code (e.g., Terraform) and CI/CD automation (e.g., GitHub Actions)
Sound judgment on the accuracy, latency, and cost trade-offs that determine whether an ML product is viable
Excellent problem-solving, debugging, and communication skills, including with non-technical stakeholders

Nice To Haves

On-device or edge inference experience (Core ML, TensorFlow Lite, ExecuTorch) and integration into mobile applications
Active learning or human-in-the-loop labeling systems
Computer vision on small, long-tail, or industrial inspection datasets rather than large public benchmarks
Experience with camera and sensor integration, or working alongside hardware teams on capture quality
Experience with robotics, IoT, or edge computing (ROS or similar platforms)
Familiarity with automotive service, dealership operations, or DMS ecosystems
Contributions to open-source projects

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
Monitor model quality in production: drift detection, segmented performance analysis, and triage of real-world failures against real inspection imagery
Define the metrics that matter commercially — false-positive and false-negative rates on a wear call, technician override rate, unit inference cost — and report against them
Improve model performance directly: architecture selection, augmentation, hard-example mining, and quantization or distillation where latency and cost demand it
Evaluate on-device versus cloud inference trade-offs for the mobile app, and own whichever path we choose
Establish MLOps foundations: reproducible training, experiment tracking, CI/CD for models, and infrastructure as code
Serve as the cloud architecture counterpart to the Staff Full Stack Engineer — reviewing designs, setting GCP best practices, and raising the platform’s infrastructure bar
Work with hardware and field operations on capture quality — lighting, focus, and probe positioning — since upstream image quality sets the ceiling on model performance
Own production support for the ML stack, including incident response and on-call participation for inference availability
Proactively identify technical risks and architectural trade-offs, and communicate them clearly to leadership