Live opening · Posted 15 days ago

Robotics Controls Engineer

Reflex Robotics · New York, United States (Remote)
Linkedin Yes
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At a glance

The key details from the original listing.

Posted 15 days ago
CompanyReflex Robotics
LocationNew York, United States (Remote)
Work modeYes
SkillsPython, C++, PyTorch
SourceLinkedin
ListedPosted 15 days ago

About the role

Description supplied by the original job listing.

Company Description
Reflex Robotics builds general-purpose, AI-powered humanoid robots for the toughest jobs in industry.
We’re focused on jobs that are genuinely hard on the human body: lifting 40-pound bags of dog food, loading boxes for an entire shift, and moving heavy materials from floor to high overhead shelves. Not just lightweight tasks in controlled environments. Actual, back-breaking work.
We build the full robot stack ourselves, from cameras and compute to actuators, electronics, and software. Our custom-built supervision stack helps handle the hard parts of deployment, from real floor variability to keeping work moving without line disruption. That supervision also generates proprietary real-world data that helps improve autonomy over time.
Role Description
You will design, implement, and optimize control algorithms for our robotic systems, with a focus on motion control, stability, and performance.
Qualifications
Strong foundation in robotics and motion control, including kinematics, dynamics, trajectory generation, and feedback control.
Experience implementing and debugging controllers on real robotic systems.
Strong C/C++ programming skills; Python experience is a plus.
Familiarity with state estimation, sensor fusion, and working with sensors and actuators.
Practical troubleshooting skills across software, hardware, and control systems.
Bachelor’s degree in Robotics, Electrical Engineering, Mechanical Engineering, Computer Science, or a related field, or equivalent practical experience.
Nice to Have
You don’t need to check every box.
Humanoid, legged, mobile manipulation, or other dynamic robotic systems.
Whole-body control, impedance control, model predictive control, or trajectory optimization.
Simulation, system identification, and sim-to-real.
Reinforcement learning, imitation learning, or other learning-based approaches to robotic control.
Experience deploying learned policies on real robots.
Robotics simulation and ML tools such as MuJoCo, Isaac, PyTorch, or JAX.

Work arrangement
Yes

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