Founding Research Engineer - Mobile Manipulation

Starhold; Palo Alto (CA); Sur place; USD 180 000 - 260 000; Plein temps

Résumé du poste

Sancho Robotics is seeking a Founding Research Engineer to lead mobile manipulation efforts in our on-site Palo Alto lab. You will design policies enabling a mobile manipulator to perform generalizable skills.

You will develop robust control and recovery strategies for contact-rich manipulation, train and evaluate policies with teleoperation data, logs, and simulation, and iteratively improve task success and platform generalizability.

Qualifications

• Hands-on experience deploying robotic manipulation policies on real-world robot systems.
• Strong robotics fundamentals in kinematics, control, motion planning, and trajectory optimization.
• Experience in C++, ROS/ROS2, and simulators like MuJoCo or Isaac Lab.
• Sound technical judgment about when to use a math- or data-driven approach.

Responsabilités

• Design and implement policies for a mobile manipulator to perform generalizable skills (grasping, insertion, tool use).
• Develop robust control, compliance, and recovery strategies for contact-rich manipulation on hardware.
• Train, evaluate, and deploy robot policies using teleoperation data, robot logs, and simulation.
• Systematically improve task success rates and generalizability through rigorous testing and failure analysis.

Connaissances

Robotics fundamentals

Policy deployment

System-level thinking

Outils

C++

ROS/ROS2

MuJoCo

Isaac Lab

Description du poste

Founding Research Engineer - Mobile Manipulation

Full-time, on-site

Who are we

Sancho Robotics builds general-purpose mobile manipulators for advanced manufacturing. Our work was featured in the opening of the NVIDIA GTC 2026 keynote. Backed by $7 million in funding, our team has won multiple best-paper awards at top robotics conferences such as RSS and IROS and brings a decade of real-world robotics deployment experience.

What you will do

Develop Mobile Manipulation Policies: Design and implement policies that enable a mobile manipulator to perform generalizable skills such as grasping, insertion, and tool use.

Develop Control & Recovery Strategies: Develop robust control, compliance, and recovery strategies for contact-rich manipulation on hardware.

Data Collection & Policy Deployment: Train, evaluate, and deploy robot policies using teleoperation data, robot logs, and simulation.

Task Iteration: Systematically improve task success rates and expand platform generalizability through rigorous testing and failure analysis.

What you will bring

• Hands‑on experience deploying robotic manipulation policies on real-world robot systems.
• Strong robotics fundamentals in kinematics, control, motion planning, and trajectory optimization.
• Experience in C++, ROS/ROS2, and simulators like MuJoCo or Isaac Lab.
• Sound technical judgment about when to use a math‑ or data‑driven approach.