Manufacturing Test Engineer – Robotics

About Tutor

We believe robots are the biggest lever we have to making the physical world a better, more affordable place. But robots are a rounding error in our society: nearly nine out of ten American factories and warehouses don’t even have a single robot, and robots have yet to make their way into our homes or places of business.

Tutor Intelligence is changing that.

Tutor is a robotics research and deployment company, building generally capable robot hardware and artificial intelligence to enable robots to go where no robots have gone before. Our robots range from a semi-humanoid with frontier dexterity approaching the human form to workhorses that do back-breaking work, moving boxes, pallets, and bulk goods for businesses from family-owned manufacturers to some of the largest brands in the world. Tutor robots are manufactured at scale and operate as a network, combining the whole fleet’s learned experience to train ever more capable robot artificial intelligence.

Founded at MIT and headquartered in Watertown, MA, our team is full stack: we design and manufacture robot hardware, train our own models, work directly with the smallest and largest end-customers, and maintain our fleet across the United States. We’re a team of researchers, engineers, technicians, operators, and sellers who love building things and solving hard problems.

Our mission is grand: robots everywhere, for the common good, raising the standard of living across North America, and then the world.

The Role Tutor Intelligence is seeking a manufacturing test engineer to design, build and own the test systems that prove every robot we build works before it ships. Our next-generation robot moves into production in early 2027 and ramps to 18 robots a day.

This mostly comes down to test strategy and test station design: deciding what gets tested where, building the fixtures and testers, setting pass/fail and control limits from real data, and running the numbers that show the line is healthy. When something fails, you find out whether it's the part, the process or the test, and fix it.

What You'll Own

• Test strategy: define what gets tested at each stage, from incoming parts to subassembly benches to end-of-line acceptance testing. Tie each test to a real failure mode, and know what each test can't catch.

• Test environment design: design and build bench fixtures, hardware-in-the-loop rigs and end-of-line testers, including instruments, power, signal switching, safety interlocks and calibration.

• Pass/fail criteria: set limits from engineering requirements and data, not guesses. Document the reasoning behind every limit, and add guard-bands where measurement uncertainty requires them.

• Statistical process control: set control limits on key test parameters such as motor current, encoder error, torque, bus voltage, CAN error counts and cycle time. Use control charts to catch drift and shifts while units are still passing.

• Measurement system analysis: run gage R&R studies on fixtures and instruments and set calibration intervals, so a fail means the robot failed and not the tester.

• Test software and data: write Python test sequences with results logged to ION by serial number. Build dashboards for first-pass yield, failure-mode Paretos, Cpk and escapes.

• Debug and root cause: take failing robots and subassemblies to a verified root cause with a scope, meter and CAN traces. Escalate design problems to Engineering with the data that proves them.

• Closing the loop: turn line failures, field returns and MRB findings into new tests, tighter limits or process fixes. Retire tests that never catch anything.

What You'll Test

• Actuators and motor drives: joint motors, servo drives, encoders, homing

• Harnesses and power: continuity and hipot, power distribution, battery and BMS

• Communications and controllers: CAN and EtherCAT, embedded controllers, firmware and configuration

• Sensors and compute: cameras, calibration, Linux/ROS compute, provisioning

Who You'll Work With

• Our production technicians and production supervisor, every day on the line

• The Production Engineering lead you report to

• Hardware and firmware engineers, who take your escalations

• Quality and RMA, who bring you field returns

Your First Year

• A written test strategy and coverage map for the robot, approved before the 2027 ramp

• Bench tests live on critical subassemblies, with documented limits and completed gage studies

• Every robot passes an automated acceptance test, with results traced to its serial number in ION

• Control charts live on the key parameters, with first-pass yield and Cpk reported weekly

• Field escapes trend down, and each one has a test added that would have caught it

What You Bring

• BS in Electrical, Mechanical, Computer or Mechatronics Engineering

• 3+ years full-time in manufacturing or production test engineering for electromechanical products

• You've designed test stations from a blank page, not just run or maintained existing ones. You can walk us through the architecture, the instruments and the fixturing.

• You've set pass/fail limits and can explain how: requirements, characterization data, guard-banding and capability analysis

• Hands-on statistical process control: control charts, Cp/Cpk, and gage R&R or other measurement system analysis

• Strong Python for test automation and data analysis; LabVIEW/TestStand is a plus

• Strong electrical debug: schematics, scopes, meters, logic analyzers, CAN traces

• You've taken a product from pilot builds into a production ramp and moved first-pass yield or test time with numbers

Backgrounds That Fit Well Manufacturing test engineers from robotics and warehouse automation, EV and battery, AV and drones, medical devices, consumer electronics, or contract manufacturers that build electromechanical products.

This Role Is Not Software QA, chip or wafer test, design-validation testing for design sign-off, or running someone else's test plan. If you haven't designed a tester and set its limits, this role will feel very different from what you've done.

Nice to Have

• Six Sigma Green or Black Belt, or formal design-of-experiments work

• Test data infrastructure: databases, MES (ION or similar), yield dashboards

• Mobile robot or robot arm experience

• Daily use of AI tools like Claude to build tools and analyze data

Why Tutor?

Whether you’re designing robots, closing deals, running deployments, or creating the processes that help us scale, your work doesn’t sit on a shelf. It shows up on real production floors, making physical work safer and more affordable.

Our team is driven by curiosity and a genuine love of building. Everyone here takes real ownership, learns fast, and holds a high bar while having each other’s backs. If you love hard problems and creating things that didn’t exist before, you’ll fit right in.