Lead Software Engineer

### Who you are
- 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
- 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
- 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

### What the job involves
- 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
- 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