Staff / Lead Full Stack Software Engineer - Mobile & Web

About ATI:

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.

Position Overview:

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
• 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

Requirements

• 8+ years of professional software development experience, with deep React and React Native expertise
• Demonstrated experience owning mobile and web deployments end to end for thousands of users - app store releases, OTA updates, and web deploys, including the failures
• Production experience shipping and maintaining cross-platform React Native applications, ideally with Expo
• Strong background in cloud platform architecture, preferably GCP - Cloud Functions, Firebase, Cloud SQL, and Docker
• Proven experience in system architecture, API design, and hands-on implementation
• Experience with CI/CD pipelines and automation tools (e.g., GitHub Actions, EAS Build, Jenkins)
• Experience with database technologies (SQL, NoSQL) and messaging systems (queues, pub/sub)
• Track record of operating and supporting a live product, not just launching one
• Strong understanding of software engineering principles including SOLID, clean code, and test-driven development
• Excellent problem-solving, debugging, and communication skills

Preferred Qualifications:

• Experience with Bun.js or other modern JavaScript runtimes in production
• Experience with on-device image processing, camera integration, or computer vision in mobile apps
• Experience integrating USB or Bluetooth peripherals with mobile applications
• Background in offline-first mobile architectures and data synchronization patterns
• 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

Why Join ATI:

• Be part of a groundbreaking startup transforming automotive service technology
• Work with a team of industry veterans and top-tier robotics and software talent
• Own a product that's already earning revenue - and the platform it becomes next
• Our customers are our investors, so you'll develop and test in real service lane environments
• Clear Total Addressable Market with strong pull from B2B partners
• Competitive salary and comprehensive benefits package
• Prime location in Woburn, MA with on-site parking
• Collaborative, low-ego, high-intensity work environment