Drimva

Nidus Technologies

Staff Hardware Engineer, Robotics Systems & Deployments

New York City · On-site · Staff · Full-time · Posted Sep 22, 2026

Experience
Typically 8+ years; no alternatives stated.
Education
Bachelor’s degree in mechanical engineering, mechatronics, robotics, electrical engineering, or a related discipline—or equivalent practical experience.

About the job

Leads the architecture, development, validation, production, and field deployment of robotic data-collection platforms, end effectors, camera systems, and customer workcells.

What you'll do

  • Define scalable hardware architectures and technical roadmaps; lead design reviews, validation strategies, and high-risk hardware programs.
  • Develop data-collection systems and manipulation hardware, coordinating with ML and data teams on how hardware affects data quality and policy performance.
  • Lead customer workcell deployments, including technical discovery, installation, commissioning, troubleshooting, and development of deployment standards.

What they ask for

  • Bachelor’s degree in mechanical engineering, mechatronics, robotics, electrical engineering, or a related discipline—or equivalent practical experience.
  • Typically 8+ years of experience developing complex mechanical, electromechanical, robotic, automation, or sensing systems.
  • Track record leading complex hardware programs from ambiguous requirements through architecture, detailed design, validation, production, and field deployment.
  • Deep proficiency with a modern 3D CAD platform such as SolidWorks, Onshape, Creo, or NX.

Nice to have

  • Experience developing robotic manipulation hardware, such as grippers, tool changers, compliant mechanisms, or tactile or force-sensing systems.
  • Experience architecting industrial robot, cobot, laboratory automation, warehouse automation, or machine-vision workcells.

Tools

SolidWorks, Onshape, Creo, NX, ISO 10218, ISO/TS 15066, ANSI/RIA R15.06

AI focus

Supports vision-language-action models for robotic manipulation through hardware architecture and data-collection systems; partners with ML and data teams on how hardware affects training-data quality and policy performance.

Model-written summary · the original posting is authoritative

First seen Oct 29, 2026 · Last verified Nov 5, 2026 (UTC)