Hands-On Test ME Technician — Mechanical Validation Pro

HireArt, Inc.; San Francisco (CA); Vor Ort; USD 39.000 - 52.000; Vollzeit

Benefits dieser Stelle

401k package

Disability and life insurance coverage

Flexible Spending Account (FSA)

Zusammenfassung

HireArt is helping our client find a Test ME Technician to support the testing and validation of mechanical systems in a hands-on hardware development lab. The role is onsite in San Francisco, CA, for a full-time, 6-month contract.

You will execute established test procedures, maintain fixtures, and record measurements to evaluate hardware performance. This work supports AI training for improved understanding of hands-on physical work.

Qualifikationen

• 2+ years of hands-on experience in mechanical testing or reliability laboratories.
• Experience setting up and maintaining mechanical test fixtures.
• Knowledge of metrology and experience using precision measurement tools, including calipers, gauges, and load cells.
• Ability to follow established test procedures and accurately document measurements and results.

Aufgaben

• Execute established mechanical test procedures to evaluate payload, reach, repeatability, backlash, torque, and cycle life.
• Set up, maintain, and troubleshoot mechanical test fixtures to support accurate and repeatable testing.
• Use calipers, gauges, and load cells to measure mechanical performance and record test results accurately.
• Review written and on-screen work instructions, identify inaccuracies, and provide feedback to improve testing procedures and documentation.

Kenntnisse

Hands-on mechanical testing

Metrology

Test documentation

Tools

Calipers

Gauges

Load cells

Jobbeschreibung

HireArt is helping our client find a Test ME Technician to support the testing and validation of mechanical systems in a hands-on hardware development lab. The role is onsite in San Francisco, CA, for a full-time, 6-month contract.

You will execute established test procedures, maintain fixtures, and record measurements to evaluate hardware performance. This work supports AI training for improved understanding of hands-on physical work.