Hardware Test Engineer

Tutor Intelligence•Watertown, MA
•$115,000 - $150,000

About The Position

Tutor Intelligence builds and deploys AI-powered robots for warehouse and factory work. The Hardware Test Engineer will define and build testing for robots and critical components, from incoming parts through final acceptance, and for parts returned from the field. The role uses manufacturing and field failure data to identify what is breaking, determine why, and recommend changes to Manufacturing and Engineering. Part of Production Engineering, this hands-on role works closely with the Lead Hardware Engineer – Production, who sets overall test strategy and mentors the team. The engineer works on the production floor debugging robots, building test systems, and improving manufacturing processes. Responsibilities include production and component test design, returned-part failure analysis, test fixtures and automation, failure tracking, and test-result traceability in the MES. Success in the first 90 days includes mapping test coverage against known failure modes, closing the largest gaps, establishing a returned-part testing and failure-analysis process, and sharing regular failure-trends reports. Within one year, the goal is for field escapes to trend toward zero, every critical component to have a defined test, test results to be traceable by serial number, and repeat failures to decline as data informs design and process changes.

Requirements

  • Bachelor's degree in Electrical, Mechanical, Computer, or Mechatronics Engineering.
  • Typically 3–6 years of experience in test, reliability, quality, or production engineering on electromechanical hardware.
  • Hands-on debugging experience using scopes, meters, and diagnostic tools across power, PCBAs, motors, sensors, and communication interfaces.
  • Experience writing test procedures, defining pass/fail criteria, and building test fixtures or automated tests.
  • Experience with failure analysis and root-cause methods such as 8D, 5 Whys, fishbone, and FMEA.
  • Comfort working with failure data to identify trends and make recommendations.
  • Daily, hands-on use of AI tools such as Claude in engineering work.
  • Willingness to work on the production floor and experience building processes where none existed.

Nice To Haves

  • Robotics, industrial automation, or startup experience.
  • Python for test automation and data analysis.
  • Reliability engineering experience, including life testing, HALT/HASS, or MTBF analysis.
  • RMA, MRB, or supplier corrective action (SCAR) experience.
  • Experience with MES and traceability systems.

Responsibilities

  • Define and build production test environments for components, subassemblies, bring-up, end-of-line testing, and Factory Acceptance Testing (FAT).
  • Write test procedures, pass/fail criteria, and test coverage to prevent failures from escaping.
  • Determine testing approaches for critical components, including robot arms, control boxes, PCBAs, power systems, sensors, and harnesses; include incoming inspection and supplier quality checks.
  • Build testing and failure-analysis processes for field-returned parts; confirm failures, find root causes, and coordinate corrective actions with suppliers, Engineering, and Manufacturing.
  • Track and analyze manufacturing and field failure data, identify trends and repeat failures, and provide recommendations to Manufacturing and Engineering.
  • Own test coverage for known failure modes; investigate field escapes and close gaps in testing.
  • Design and build fixtures, automated test scripts, and diagnostic tools to make testing faster and more repeatable and reduce reliance on operator skill.
  • Debug robots on the production line, drive corrective actions, and work with production engineers to improve processes that cause defects.
  • Ensure test results are recorded in the MES and tied to robot and component serial numbers for traceability.
  • Map existing test coverage, establish returned-part testing and failure analysis, and provide regular failure-trends reporting.
  • Use failure data to drive design and process changes and reduce field escapes and repeat failures.
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