Mechanical Design Engineer

Gradient Robotics

San Francisco (CA)

On-site

USD 90,000 - 120,000

Full time

14 days+

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Job summary

Gradient Robotics is seeking a Mechanical Engineer to own subassemblies from first sketch to validated hardware. This hands-on role requires designing a part, performing hand calcs and basic FEA, fabricating, and testing on the robot, with rapid iteration on failures within the same week.

You will work closely with a small team in San Francisco, on-site five days a week (six during crunch times), building components that scale AI infrastructure worldwide.

Qualifications

  • 1-4 years of mechanical design experience (exceptional new grads with strong project or internship work are welcome)
  • Solid fundamentals: statics, materials, tolerance analysis, fasteners, and basic FEA
  • Experience building things: student teams, products that shipped, prototypes that worked
  • Hands-on machining experience: we have some really cool machines, and as a small team, engineers do need to make their own parts at times
  • Able to work on-site in San Francisco 5 days/week (6 days/week if needed during crunch time), embedded in the team

Responsibilities

  • Own a subassembly and its parts through design, hand calcs, FEA, fabrication, assembly, and validation
  • Own the supporting analysis: hand calcs, tolerance stack-ups, fastener and material selection, and basic FEA
  • Build, assemble, and test your own parts on test fixtures and on the robot
  • Iterate fast: turn feedback from a failed test into a revised part the same week

Skills

Statics
Materials
Tolerance analysis
Fasteners
Basic FEA
Hand calcs
Machining
On-site work

Tools

Fusion 360

Job description

About the Company

The datacenter buildout is the largest industrial project in human history. Gradient builds the autonomous robots that make it possible.

Partnered with the world's largest AI infrastructure companies and backed by the industry's best investors, we move fast and build full-stack systems that matter.

About the role

We're looking for a Mechanical Engineer to be the backbone of how Gradient Robotics turns designs into working hardware. This is intentionally a hands-on ownership role. You'll own a subassembly and its parts from first sketch to validated hardware while partnering closely with the rest of the engineering team on whatever is highest leverage at any given moment. This is a role for someone who loves the full build-test loop: you'll design a part, do the analysis to back it up, make it, test it on the robot, and turn a failed test into a revised part the same week. You'll touch every stage of your hardware, which means real ownership and room to grow quickly. The impact is significant: the subassemblies you own go straight onto the machines that scale AI infrastructure worldwide, and nothing you design gets handed off halfway.

Responsibilities
  • Own a subassembly and its parts through design, hand calcs, FEA, fabrication, assembly, and validation

  • Own the supporting analysis: hand calcs, tolerance stack-ups, fastener and material selection, and basic FEA

  • Build, assemble, and test your own parts on test fixtures and on the robot

  • Iterate fast: turn feedback from a failed test into a revised part the same week

Minimum Qualifications
  • 1-4 years of mechanical design experience (exceptional new grads with strong project or internship work are welcome to apply)

  • Solid fundamentals: statics, materials, tolerance analysis, fasteners, and basic FEA

  • Experience building things: student teams, products that shipped, prototypes that worked

  • Hands-on machining experience: we have some really cool machines, and as a small team, engineers do need to make their own parts at times

  • Able to work on-site in San Francisco 5 days/week (6 days/week if needed during crunch time), embedded in the team

Preferred Qualifications
  • Experience at a hardware, robotics, aerospace, or automotive company

  • CAM experience (Fusion 360) or comfort programming your own toolpaths

  • Comfort using AI tools to multiply your own output

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