Senior / Staff Robotics Engineer, Motion Planning

Why RoboForce

RoboForce is an AI robotics company developing Physical AI–powered Robo-Labor for dull, dirty, and dangerous work. The company's robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability.

We are seeking a Senior / Staff Robotics Engineer, Motion Planning to own the local mobility and safety stack for our fleet. In this role, you will bridge the gap between AI-driven perception and physical hardware execution. You will consume traversability and semantic costmaps generated by our AI team and translate them into safe, dynamically feasible, and deterministic motion.

You are solving the physics of mobility on unpredictable terrain, ensuring our humanoid platform respects its physical limits, shifting center of mass, and traction constraints.

Responsibilities

• Dynamic Motion Planning: Architect and implement advanced local planners (e.g., optimization-based approaches like MPC, MPPI, or custom contouring control) that translate AI-generated routes into trajectories that a heavy, non-holonomic base can physically execute on uneven terrain.
• AI Integration & Costmap Consumption: Build the high-performance C++ pipelines that ingest the AI team's neural network outputs (semantic maps, learned costmaps) in real-time and convert them into deterministic mathematical constraints for the planner.
• Center of Mass (CoM) Aware Navigation: Ensure local planners dynamically adjust to real-time changes in the robot's Center of Mass caused by the lifting column and dual-arm payloads. Your trajectories must guarantee stability and prevent tipping on slopes or rough ground.
• Deterministic Safety Architectures: AI models hallucinate. You will build the deterministic C++ safety layer and fallback behaviors that override AI inputs if a commanded path violates physical safety constraints or if off-the-shelf SLAM tracking drops.
• WBC Integration: Architect the local planning interfaces to seamlessly pass base trajectories, velocity limits, and dynamic constraints to our upcoming Whole-Body Control (WBC) architecture, bridging the gap between base mobility and upper-body manipulation.

Requirements

• Education: Ph.D. in Robotics, Mechanical Engineering, Computer Science, or a related field, OR an M.S. degree with 4+ years of relevant industry experience.
• Motion Planning Expertise: Deep hands-on experience developing and tuning advanced local planners and trajectory optimization algorithms for mobile robots in complex, unstructured environments.
• Rigid Body Dynamics: Strong understanding of kinematics, vehicle dynamics, and center-of-mass constraints for top-heavy mobile robots operating on uneven outdoor terrain.
• C++ Performance Engineering: Expertise in modern C++ (C++17/20), with strict attention to real-time performance, memory management, and low-latency execution. You must be comfortable bridging high-latency AI outputs with hard real-time control loops.
• Field Deployment: Proven track record of building and deploying autonomous navigation stacks on real, physical robotic platforms in the wild.
• Requires 5 days/week in-office collaboration with the teams.
• Bonus Qualifications
• WBC / Manipulation: Familiarity with Whole-Body Control frameworks, operational space control, and how mobile bases synchronize with high-DOF manipulators.
• Applied SLAM / State Estimation: Experience tuning, debugging, or filtering outputs from commercial SLAM/VIO systems when they fail in visually degraded environments.
• Slip & Traction Estimation: Background in detecting slip or loss of traction from proprioceptive sensor signals and adapting planning constraints accordingly.
• GPU Acceleration: Experience using CUDA to accelerate grid processing or trajectory sampling pipelines.