Palo Alto, California
R&D Engineer – Humanoid Core Technologies
Since its founding in 2015, 1X has been at the forefront of developing advanced humanoid robots designed for household use. Our mission is to create an abundant supply of labor via safe, intelligent humanoids.
We strive for excellence in all we do, solving some of the hardest problems in robotics with the world’s most talented individuals. Every part of our robots is designed and produced in-house—from motor coils to AI—reflecting our vertically integrated approach. At 1X, you’ll own real projects, be recognized for your achievements, and rewarded based on merit.
We believe the best work is done when collaborating and therefore require in-person presence in our office locations.
Why this job is exciting
Join the NEXT team shaping the foundations of the future humanoid robot
Tackle unsolved challenges like human-level walking, dexterous manipulation, and embodied perception
Push the boundaries of research into practical, buildable solutions—fast
Responsibilities
Research, design, and prototype fundamental humanoid technologies including actuation systems, robotic joints, and structural components
Model and simulate systems to evaluate performance and feasibility
Design experiments and analyze results to validate hypotheses and inform engineering tradeoffs
Build and iterate on physical prototypes using machining, rapid fabrication, and embedded systems
Evaluate academic research and patents for technical inspiration and practical application
Distill high-concept ideas into actionable engineering workstreams
Collaborate closely with other domain experts to bridge theory and application
Requirements
Strong grasp of core engineering principles including math, mechanics, and materials
Experience designing actuation systems—motors, drivetrains, joints, structures
Background in Mechanical Engineering, Electrical Engineering, Physics, or similar
Demonstrated ability to go full-stack in R&D: from concept to prototype to evaluation
Skilled at assessing feasibility and filtering high-potential ideas from theoretical noise