Member of Technical Staff - Robotics
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- Work arrangement
- hybrid
- Employment
- FullTime
- Stated compensation
- Not specified
- Stated sponsorship
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1 with evidence8 unclear or unstated- Remote policy
- hybrid
“Provider workplace type: hybrid”
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- FullTime
9 fields not stated in this posting
- Employer sponsorship
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About Us
Veeda AI is building the next generation of multimodal foundation world models for Physical AI. We're a small, fast-moving team of engineers and researchers from leading AI labs, tackling some of the most challenging problems at the intersection of AI, robotics, and embodied intelligence. If you're excited about pushing the boundaries of what's possible with Physical AI, you'll have the opportunity to make an outsized impact from day one.
Responsibilities
Robotics Systems & Data: Build and maintain robotic systems and data-collection workflows that support research and evaluation.
Real-World Experimentation: Design and run hardware experiments to assess performance, robustness, and generalization.
Evaluation & Analysis: Develop reproducible benchmarks, investigate failure modes, and use findings to guide model and system improvements.
Research Collaboration: Work closely with researchers and engineers to connect advances in machine learning with practical robotics challenges.
Requirements
PhD degree or equivalent hands-on experience in Robotics, Computer Science, Mechanical or Electrical Engineering, or a related technical field.
Experience developing learning-based robot policies and deploying them on real hardware.
Strong Python and PyTorch skills, with proficiency in a robot software stack.
Solid understanding of robot kinematics, sensing, calibration, and system integration.
Ability to independently design, execute, and analyze reproducible robotics experiments.
Willingness and ability to travel for hands-on robot setup, testing, and deployment.
Nice to Have
Experience with manipulation, locomotion, or whole-body control.
Experience establishing a robotics lab or data-collection operation from scratch.
Experience with imitation learning, reinforcement learning, or robot foundation models.
Experience with robotics simulation and transferring learned behaviors across environments.
Experience working with multimodal sensor data, teleoperation systems, or tactile and force feedback.
Publications or substantial contributions to research on robot learning or embodied AI.
Contributions to open-source robotics projects or research infrastructure.
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