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Overview.ai in San Francisco is seeking a Machine Vision Applications Engineer to own imaging feasibility in the SF lab. You will run trials with real parts, determine lighting, lens, camera setups, and prove imaging can be deployed reliably in production.
You will work hands-on with hardware, document results, and collaborate with sales and engineering to shape hardware decisions and deployment approaches, with a clear path to becoming the technical voice on imaging as the team scales.
Machine Vision Applications Engineer
San Francisco, CA · Applications · Full-time, in office
Overview.ai is bringing the cutting edge of AI computer vision to manufacturing, solving inspection problems that were previously not solvable with traditional machine vision. We’re a full-stack company: we deploy GPU-powered cameras on production lines, run inference on the edge, and operate a platform that supports large fleets of devices deployed across the world.
We’re growing extremely fast. Our customers love the product because it works: high accuracy, fast deployment, and an operator-friendly experience that makes real factory rollouts possible, not just pilots.
We’re looking for a Machine Vision Applications Engineer to own imaging feasibility in our San Francisco lab.
Customer samples arrive here before anything gets deployed. Your job is to make the defect visible: work out the lighting, the lens, the camera, the geometry, and prove the imaging holds up repeatably. That work is what every successful deployment is built on, and right now it is the bottleneck between a customer’s hardest inspection problem and a system that solves it.
This is a hands‑on role. You will spend your days with real parts, real optics, and real lighting, running trials and documenting what works and why. If you have been doing this inside a vision vendor, a distributor, or an integrator and want the same work with more ownership and a harder class of problem, this is that job.
You own the lab: The SF lab is yours to run, from testing standards to hardware evaluation to how we decide an application is feasible.
Hard problems, by design: Customers come to us with inspections that defeated traditional machine vision for years. The easy imaging problems never reach your bench.
Volume and variety: A growing stream of customer samples across automotive, aerospace, and consumer manufacturing means you see more imaging problems in a year here than most engineers see in five.
Your findings travel: What you learn in the lab shapes hardware decisions, deployment approaches, and what we tell customers is possible.
Room to grow into it: This is a mid‑level role with a clear path to becoming the technical voice on imaging as the lab and the team scale.
Imaging feasibility: Run trials on incoming customer samples to determine whether and how an inspection can be imaged reliably.
Lighting and optics selection: Choose and configure lighting, lenses, cameras, filters, and geometry for each application, including the parts that fight back.
Lab ownership: Set up and run the SF lab: test standards, hardware inventory, sample handling, and how results get recorded.
Proof‑of‑concept testing: Build and document POCs that give Sales and Applications a defensible answer on what the system can and cannot do.
Image quality troubleshooting: Diagnose and resolve image quality problems that drive false rejects, missed defects, and inconsistent results.
Hardware evaluation: Test new cameras, lenses, and lighting and recommend what belongs in our stack.
Findings and documentation: Write up what worked, what did not, and why, so the next engineer facing the same part does not start over.