About Tutor
We believe general-purpose, generally-intelligent robots will be built in our lifetimes. Robots will work in our factories, move our goods, walk on our streets and eventually be in our homes. To build that future, research and deployment must work in lockstep: real-world operation must make the technology better and better technology must make deployment easier. We're looking for the thinkers, builders, and researchers who want to be part of that loop.
As an AI robotics company that deploys its inventions directly into the facilities that need them, on state-of-the-art hardware, everything we build at Tutor has a direct impact on the global, physical economy. In 2026 we opened our first manufacturing facility in Watertown, MA, and we're scaling to 1,000+ robots in the field by 2028.
Our Culture
We believe that something truly special can happen when talented, motivated people work together; at Tutor, every member of our team is empowered to have real impact in everything that they do. We're characterized by both technical excellence and next-level collaboration and respect.
Why This Role Matters
Every robot we ship goes to work at a customer's site. A failure there costs us far more than one caught on our floor. We need one engineer who owns two answers: are we shipping good robots, and what is failing and why.
The Role
You're our first quality and reliability engineer: the hardware engineer dedicated to quality and reliability for production, building the function from the ground up. You report to the Head of Manufacturing and have the authority to hold any robot that doesn't meet the bar.
Your first priority is returned material: containment, MRB, failure analysis and supplier RMAs, tracked in tools you build with AI. Ship and incoming quality gates come next.
Up to 25% travel to customer sites and suppliers.
Example projects you might work on:
- A failure tracker that ties every returned robot arm joint to its serial number, build and supplier, and shows which joints fail and why
- Incoming inspection and test for refurbished robot arms before they reach the line
- A screening process for a batch of suspect control boxes, from quarantine through supplier return
- An automated ship gate that pulls test results from our MES and blocks a robot that's missing a check
- A weekly fleet quality report that Engineering, Deployment and Customer Success actually use
What You'll Own
- Containment and MRB: When a failure is found, contain it. Quarantine suspect stock and identify every robot in the factory and field that could be affected. Run our weekly Material Review Board: disposition, RCA owners and corrective actions to closure.
- Failure analysis on returned material: Own FA on every returned part and field failure, including robot arms, control boxes, PCBAs, drives, power systems, sensors and harnesses. Direct technicians on teardowns and bench tests, make the root cause call and verify the fix works.
- One source of truth: Use AI tools like Claude to build dashboards and automated checks on top of our MES, field tickets and robot telemetry, showing failures by component, supplier and serial number, repeat failures and escapes. Give Engineering, Manufacturing, Deployment and Customer Success a regular, data-based view of where quality stands, and feed it into design reviews.
- Supplier RMAs: Be the technical point of contact with suppliers on parts we RMA. Send failure data and FA results, push for their root cause, issue SCARs and hold them to corrective actions, credits and replacements.
- Quality gates: Set the pass/fail criteria for incoming parts and finished robots, and make the ship/hold call. When a failure exposes a test gap, define what's needed, Production Engineering builds it, and you verify it catches the failure.
What Success Looks Like
- 90 days: Every returned part has an owner, a status and a root cause path. MRB runs weekly on data. Every open supplier RMA has FA results and a supplier response. A live failure dashboard is in use across teams.
- 1 year: Field escapes are trending toward zero. Repeat failures on our top failure modes are down measurably. Incoming and ship gates are defined and largely automated. Leadership gets quality answers from your data, not from meetings.
Required
- You've personally driven production or field failures on electromechanical hardware (robots, automation, vehicles, industrial equipment or similar) to a verified root cause and corrective action.
- 4+ years in quality, reliability, test or hardware engineering.
- Bachelor's in Electrical, Mechanical, Computer Engineering or a related field.
- Experience working directly with suppliers on RMAs and corrective actions.
- You've built tools with AI (scripts, dashboards or automated checks) that other people used. Be ready to walk us through one.
- Python or SQL to analyze failure data and turn it into clear conclusions.
- Able to travel up to 25%.
Preferred
- Robotics, industrial automation or hardware startup experience.
- Hands-on electrical debug on motor drives, power and communication faults.
- Failure analysis on robot arms, servo motors or gearboxes.
- Reliability engineering: life testing, HALT/HASS, Weibull and MTBF analysis.
- MES and serial-number traceability systems.
- ASQ CQE or CRE certification.
Why Join
- Build the function. You're our first quality and reliability hire. You set the standard, choose the tools and shape the team that grows around you.
- Real authority from day one. You report to the Head of Manufacturing and can hold any robot that doesn't meet the bar. Your call sticks.
- Hard, real failures. Robot arms, drive systems, sensors and power electronics running full shifts at customer sites. Every root cause you close shows up in the field.
- AI is how we work. You'll have the tools and the backing to automate the quality work most companies still do by hand.
- Scale with us. Go from a new production line to 1,000+ robots in the field by 2028, with quality built in from the start.
How We Work: Our Values in This Role
We hire against our values, and we'll interview you against them too. Here's what they look like in this seat:
- Love of the Game. A teardown that proves a root cause is satisfying to you, and you want the data to be airtight.
- Gumption. You don't wait for a failure to be assigned. You spot the trend, open the investigation and chase the supplier until it's closed.
- Beginner's Mindset. You question every "known issue" and every test we've always run. If the data says we're wrong, we change.
- Belief in Each Other. You hold engineers, techs and suppliers to a high bar, and you share the facts even when they're uncomfortable.
- Keep Moving Forward. You contain fast, ship what's good and keep digging on what isn't. Perfect analysis never stalls the line.
- Customer Obsession. A defect that escapes is a robot down at a customer site. You hold the line so they don't have to.
