JOB SUMMARY

The Lead Software Engineer - Dexterous Manipulation is a core contributor to our robot’s ability to interact with the world with human-like precision. This role is responsible for leading the development of learning-based dexterous control algorithms that unlock the full potential of state-of-the-art robotic hand hardware.

This role will bridge the gap between cutting-edge research and scalable, reliable production software. Whether leveraging reinforcement learning, imitation learning, teleoperation retargeting, or classical control, they will ensure our robots can perform complex, high-DOF tasks in both simulation and reality. As a technical lead, they will not only design the core software architecture but also influence hardware design to achieve world-class manipulation capabilities.

ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES

• Strategic Ownership: Serve as the technical authority for dexterous manipulation. Create the long-term technical roadmap, ensuring hand control and multi-fingered coordination capabilities outpace industry standards.
• Architectural Definition: Design and enforce the foundational software frameworks for manipulation. Own the decision-making process for balancing autonomous logic with high-fidelity teleoperation, ensuring the architecture is scalable for future hardware generations.
• Research & Innovation: Perform and direct the integration of state-of-the-art research. Select and deploy the specific learning-based policies and vision-integrated systems that will define system physical capabilities.
• Sim-to-Real Ownership: Lead the strategy for high-fidelity simulation. Set the standards for success in virtual environments to optimize policy transitions to physical fleet hardware.
• Hardware Design Influence: Drive the specifications for next-generation hardware. Define the requirements for sensing, degrees of freedom, and torque profiles.
• Production Excellence: Oversee the transition from experimental research to fleet-wide deployment. Ensure performance and reliability of C++/Python code running on production-level assets.
• Technical Leadership & Culture: Act as a force multiplier across the organization. Beyond code reviews, foster a culture of technical rigor, setting the bar for architectural excellence and mentoring the next generation of robotics leaders.

SKILLS AND REQUIREMENTS

Technical Skills (Must-Have)

• Dexterous Manipulation: Deep expertise in multi-fingered hand control, grasp planning, and in-hand manipulation, with a strong track record of successful hardware deployment.
• Advanced Control & Learning: Proficiency in learning-based control for robotics (e.g. flow/diffusion-based visiomotor policies, reinforcement learning, reward modeling, etc.).
• Software Engineering: Proficiency in Python, with experience building real-time robotic software stacks.
• Simulation Environments: Experience with physics engines such as IsaacSim, MuJoCo, or Drake for policy training and validation.

Good to Have

• Robotic Kinematics: Strong foundation in spatial transformations, Jacobian-based control, and constrained optimization.
• Teleoperation: Experience with VR/haptic interfaces and retargeting algorithms for human-in-the-loop control.
• Tactile Sensing: Experience integrating tactile/haptic feedback into manipulation pipelines.
• Computer Vision: Familiarity with 6D pose estimation, point cloud processing, or visual-servoing.
• Hardware Bring-up: Experience with the initial calibration and tuning of high-DOF robotic end-effectors.

EDUCATION and/or EXPERIENCE

• BS/MS/PhD in Robotics, Computer Science, Electrical Engineering, or a related field.
• 5+ years of relevant experience (or 3+ years with a PhD) specifically focused on robotic manipulation or complex motion control.
• A proven track record of taking complex algorithms from a research/simulation environment and successfully deploying them on physical hardware.

PHYSICAL REQUIREMENTS

• Prolonged periods of sitting at a desk and working on a computer 
• Must be able to lift 15 pounds at times
• Vision to read printed materials and a computer screen
• Hearing and speech to communicate 

JOB SUMMARY:

The Senior Reinforcement Learning  Engineer is a key, hands-on role focused on achieving state-of-the-art performance on our humanoid robots. This engineer will leverage their deep expertise in RL to solve critical locomotion and manipulation challenges and deliver breakthrough results on physical hardware. The primary focus of this role is to rapidly implement, iterate, and deploy advanced learning algorithms to push the boundaries of what our robots can do. As a senior member of the team, this individual will also be responsible for mentoring junior engineers, elevating the team's overall technical capabilities through their guidance and expertise.

ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES:

• Implement and deploy state-of-the-art RL algorithms to achieve ambitious, world-class performance on dynamic locomotion and manipulation tasks with physical hardware.
• Drive the entire development cycle, from prototyping in simulation to robustly transferring and fine-tuning policies on the robot.
• Optimize and scale the RL training pipeline for faster iteration, contributing to core infrastructure for high-throughput simulation and distributed training.
• Mentor junior engineers by providing technical guidance, conducting insightful code reviews, and sharing best practices in reinforcement learning and software development.
• Collaborate closely with the robotics and hardware teams to diagnose system-level issues and co-develop solutions that enable more complex learned behaviors.
• Analyze and present hardware results to guide future technical directions and demonstrate progress on key company objectives.
• Develop and refine motion retargeting pipelines to translate human demonstration data (mocap, teleoperation) into robust reference trajectories for reinforcement learning.

 SKILLS AND REQUIREMENTS

• Deep, hands-on expertise (5+ years) with common RL frameworks (e.g., PyTorch, JAX) and high-fidelity physics simulators (e.g., MuJoCo, IsaacGym)
• Mastery of Python for rapid prototyping and training, alongside strong proficiency in C++ for developing performant, deployable code.
• Experience building or utilizing large-scale, distributed training pipelines and a strong intuition for their optimization.
• A strong theoretical understanding of modern reinforcement learning, including deep expertise in areas like imitation learning, model-based RL, and sim-to-real transfer techniques.
• A strong intuition for robot dynamics and controls theory, with the ability to apply these principles to guide and constrain learning-based approaches.
• A results-oriented mindset with a passion for seeing complex algorithms work on real-world hardware.

EDUCATION and/or EXPERIENCE:

• A PhD or MS in Computer Science, Robotics, or a related field, with 2+ years industry experience strongly preferred.
• A proven track record of successfully deploying learning-based policies on physical robotic systems, especially legged robots or manipulators.
• Demonstrated experience mentoring or providing technical guidance to other engineers in a team environment.
• A strong publication record in relevant conferences or journals (e.g., CoRL, RSS, ICRA) is a significant plus.

PHYSICAL REQUIREMENTS:

• Prolonged periods of sitting at a desk and working on a computer
• Vision to read printed materials and a computer screen
• Hearing and speech to communicate

JOB SUMMARY:

The Reinforcement Learning  Engineer is a key, hands-on role focused on achieving state-of-the-art performance on our humanoid robots. This engineer will leverage their deep expertise in RL to solve critical locomotion and manipulation challenges and deliver breakthrough results on physical hardware. The primary focus of this role is to rapidly implement, iterate, and deploy advanced learning algorithms to push the boundaries of what our robots can do. As a senior member of the team, this individual will also be responsible for mentoring junior engineers, elevating the team's overall technical capabilities through their guidance and expertise.

ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES:

• Implement and deploy state-of-the-art RL algorithms to achieve ambitious, world-class performance on dynamic locomotion and manipulation tasks with physical hardware.
• Drive the entire development cycle, from prototyping in simulation to robustly transferring and fine-tuning policies on the robot.
• Optimize and scale the RL training pipeline for faster iteration, contributing to core infrastructure for high-throughput simulation and distributed training.
• Mentor junior engineers by providing technical guidance, conducting insightful code reviews, and sharing best practices in reinforcement learning and software development.
• Collaborate closely with the robotics and hardware teams to diagnose system-level issues and co-develop solutions that enable more complex learned behaviors.
• Analyze and present hardware results to guide future technical directions and demonstrate progress on key company objectives.
• Develop and refine motion retargeting pipelines to translate human demonstration data (mocap, teleoperation) into robust reference trajectories for reinforcement learning.

 SKILLS AND REQUIREMENTS

• Deep, hands-on expertise (5+ years) with common RL frameworks (e.g., PyTorch, JAX) and high-fidelity physics simulators (e.g., MuJoCo, IsaacGym)
• Mastery of Python for rapid prototyping and training, alongside strong proficiency in C++ for developing performant, deployable code.
• Experience building or utilizing large-scale, distributed training pipelines and a strong intuition for their optimization.
• A strong theoretical understanding of modern reinforcement learning, including deep expertise in areas like imitation learning, model-based RL, and sim-to-real transfer techniques.
• A strong intuition for robot dynamics and controls theory, with the ability to apply these principles to guide and constrain learning-based approaches.
• A results-oriented mindset with a passion for seeing complex algorithms work on real-world hardware.

EDUCATION and/or EXPERIENCE:

• A PhD or MS in Computer Science, Robotics, or a related field, with 2+ years industry experience strongly preferred.
• A proven track record of successfully deploying learning-based policies on physical robotic systems, especially legged robots or manipulators.
• Demonstrated experience mentoring or providing technical guidance to other engineers in a team environment.
• A strong publication record in relevant conferences or journals (e.g., CoRL, RSS, ICRA) is a significant plus.

PHYSICAL REQUIREMENTS:

• Prolonged periods of sitting at a desk and working on a computer
• Vision to read printed materials and a computer screen
• Hearing and speech to communicate