Senior Robotics Engineer, Navigation & SLAM

RoboForce•Milpitas, CA

1d•Onsite

About The Position

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 Robotics Engineer, Navigation & SLAM to own the mobility stack for our fleet. In this role, you will enable robust autonomy in environments where standard 2D navigation breaks down—architecting state estimation systems that operate reliably in GPS-denied zones and building traversability analysis pipelines that determine whether terrain is safe to cross. You are not just solving point-to-point navigation; you are solving the physics of mobility on unpredictable terrain.

Requirements

Experience: Ph.D. in Robotics, Mechanical Engineering, Computer Science, or a related field, OR an M.S. degree with 4+ years of relevant industry experience.
Probabilistic Robotics: Deep understanding of state estimation theory, including Kalman Filters and/or Factor Graphs. You are comfortable modeling sensor noise, uncertainty, and covariance in non-ideal conditions.
SLAM Expertise: Proven experience building, deploying, or maintaining SLAM systems on real robotic platforms.
3D Navigation: Strong grasp of path planning in 3D environments, including costmap generation from point clouds and the use of sampling-based or optimization-based planners.
C++ Performance Engineering: Expertise in modern C++ (C++17/20), with strong attention to real-time performance, memory management, and efficient processing of high-bandwidth sensor streams.
Requires 5 days/week in-office collaboration with the teams.

Nice To Haves

GNSS / RTK Fusion: Experience integrating GPS or RTK receivers into localization stacks for global positioning in outdoor environments.
Slip & Traction Estimation: Background in detecting slip or loss of traction from sensor signals and adapting planning or control constraints accordingly.
GPU Acceleration: Experience using CUDA to accelerate perception, grid generation, or navigation pipelines.

Responsibilities

Robust State Estimation: Architect and implement high-fidelity localization systems that fuse multi-modal sensor data, including LiDAR, visual odometry, IMU, and proprioceptive signals. You will build and maintain Kalman Filter or Factor Graph backends that achieve reliable accuracy under vibration, low-texture conditions, sensor occlusion, and dynamic environments.
3D Traversability Analysis: Convert raw sensor data into actionable navigation representations. You will develop real-time algorithms to analyze terrain geometry—estimating slope, roughness, step height, and clearance—to determine whether a path is safe given the robot’s mobility constraints.
Kinematically Constrained Planning: Design global and local planners that respect the physical limits of the platform, including non-holonomic constraints, stability margins on slopes, and traction limits on soft or uneven ground. Your planners must generate trajectories that are dynamically feasible, safe, and robust.