About Tutor We believe robots should be the next chapter in making physical labor safer and more affordable. But, nearly nine out of ten American factories and warehouses don’t have a single robot. Tutor Intelligence is changing that. We build robots that go straight into real factories and warehouses, to do real work, with real impact. Our robot Cassie does back-breaking work, moving boxes, pallets, and bulk goods for family-owned manufacturers to some of the largest brands in the world. Our robot Sonny does handwork, using two arms to achieve human-like dexterity in picking and packing. Every robot learns on the job, and each one we deploy makes the next one more capable, more affordable, and faster to put to work. Founded at MIT and headquartered in Watertown, MA, we do it all in-house: the robotics hardware, the machine learning, and the field deployment and service. 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. 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 of 160+ 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. The Role Tutor Intelligence robots run production on customer floors across North America. The fleet is growing faster than the tools behind it. This role exists to fix that by building. The job is to find the parts of the operation that are held together with tape and replace them with something that works. Each project starts with a problem and very little else: no spec, no existing tool, no one to hand the work to. The person in this seat figures out what should exist, builds the first version, puts it in front of the people who need it, and keeps improving it until it runs on its own. Then it's on to the next one. The role reports to the Head of Robot Operations and Reliability. What the work looks like Most of what it takes to run a much larger fleet hasn't been built yet. This role builds it. That means looking at how the operation runs today, finding where it breaks or slows down, and building what should be there instead. The work is tools, data, and the way the work itself gets done. The work will keep changing as the fleet grows. What stays the same is the job itself: see the problem, build the fix, put it to use, and keep improving it until it runs on its own. The hard failures Some robot failures don't make sense at first. A sensor tests fine on the bench and fails on the floor. A robot runs clean for three weeks and then stops every afternoon at 2:00. This role leads those investigations. That means pulling logs, reading field reports and parts history, talking to the technician who was there, building a theory, testing it, and staying with it until the cause is found and the fix holds. Who this is for Someone who asks why a problem happened, not just how to make it stop, and who looks for the structure behind it: the process, the data, or the handoff that let it happen. Someone who, after watching a team struggle with the same problem twice, has already sketched a fix that keeps it from happening a third time, and built the first version. Someone who would rather own a result than report on one. This can be an early-career role for someone with clear momentum, or a step up for someone five or more years in who is done reporting on problems they could have fixed. Either way, someone who learns fast, gets noticeably better every quarter, and wants the job to grow with the fleet. Projects on the list • A training system that gives a field technician real-time instructions on the robot in front of them, built from what the fleet has already learned about that failure. • Issue tracking that joins robot telemetry to what the technician saw on the floor, so an investigation goes from symptom to root cause and the fix can be checked across the fleet. • Predictive tooling that flags a robot likely to fail before it does, from telemetry and parts history. • Capacity planning for a regional network of technicians: where they should be based, how many, and how fast they need to reach a robot. • The reliability data itself: what gets captured, how it connects, and which numbers are worth tracking. How the work gets done • Code is a daily tool here, and so are AI agents. Build systems where agents pull the data, run the checks, and draft the write-ups, so the work runs without a person in the loop. • Ship a rough first version early, put it in front of real users, and improve it from what they say. • Work side by side with engineering, customer success and field technicians. Much of the best input comes from the people closest to the robots. • When something finally works, write it down so the next person can run it without asking. Required • 2–7 years as a technical program manager, industrial engineer, or engineer, or in operations or a similar technical role. • A track record of building. At least one system, tool or program designed from scratch and put into real use, with a result that can be stated in numbers. • Comfortable building software with AI coding tools and agents, or ready to pick it up fast. The language matters less than having shipped something that works. • Clear thinking, and the ability to turn data into a clean picture that others can act on. • Clear writing for engineers, customers and leadership. • A degree in engineering or a technical field, or equivalent experience. Stands out • Has started something at a past job that didn't exist before, and it's still running. • Experience in robotics, automation, manufacturing, logistics or fulfillment. • Builds outside of work too: side projects, a home lab, something made just to see if it could be done. Probably not the right fit for someone who • Prefers a clear task list and regular check-ins. • Would rather maintain an existing system than build a new one. • Wants the same week, every week. Why join • Few places hand one person this much ownership of how a fleet runs. • What gets built here becomes how a robotics company runs its fleet. • The role grows as the fleet grows. Benefits • Fully employer-covered health insurance, so you’re protected without the premium. • A managed 401(k) to help you plan for the future. • Generous time off: unlimited PTO and all federal holidays off for full-time salaried employees, and 16+ days of accrued paid time off plus 1.5x pay on federal holidays for hourly employees. • Regular in-office meals and team social events throughout the year. Compensation The base salary range for this role is $100,000 to $140,000, depending on experience. Location and travel In office in Watertown, MA. Travel to customer sites and regional teams as needed, roughly 10–25%. To apply Along with a résumé, send a short note about something you built: what the problem was, what you made, and what happened after. Links, photos and code are all welcome. As Tutor grows, so will our benefits. We’re continuing to invest in ways to support our team at work and beyond. Tutor Intelligence is an equal opportunity employer and welcomes applicants from all backgrounds.