Autonomous Driving / 2D & 3D SLAM Engineer

About Omakase Robotics Omakase Robotics is building Omakase OS — a software and systems stack that turns robots into reliable workers for human spaces. We started from software, but quickly learned that software alone is not enough for real-world deployment. Making robots useful in hospitals, hospitality, and retail requires hardware, autonomy, interaction, and operations designed together. Our focus is not lab demos — it's robots that work in the field. We partner with leading platforms including Unitree and are actively validating robots in real environments such as hospitals. Our long-term goal: make robots practical, affordable, and widely deployable. About this Role As a SLAM Engineer at Omakase Robotics, you will own the localization, mapping, and state estimation systems that enable our robots to navigate hospitals, hotels, and retail environments — not just flat warehouse floors. This is a harder problem than what most SLAM engineers work on, and the impact is immediate: our robots are already in real environments and the systems you build will be deployed quickly. Key Responsibilities Develop, evaluate, and deploy robust 2D/3D SLAM and state-estimation algorithms for real-world service environments Design and implement sensor fusion algorithms (EKF/UKF, graph-based optimization) integrating LiDAR, cameras, IMUs, and GNSS Implement and optimize path planning algorithms (Hybrid A*, TEB, DWA) for dynamic indoor environments Optimize algorithms for real-time performance on edge computing platforms and SoCs Develop online/offline sensor calibration toolchains Integrate SLAM into the full-stack autonomous software framework with perception, control, and simulation teams Apply latest research (NeRF, 3D Gaussian Splatting) to production mapping challenges Required Qualifications Master's or PhD (or equivalent professional experience) in Robotics, CS, or Computer Engineering 3+ years of professional or strong academic experience in 2D/3D SLAM, Visual Odometry (VO), or VIO Production-level C++ (C++11/14/17) and solid Python skills Strong mathematical foundation: 3D geometry, linear algebra, coordinate transformations, non-linear optimization (Ceres, g2o) Hands-on experience with ROS/ROS2 in Linux development environments Experience processing real sensor data: 3D LiDAR, depth/RGB cameras, IMUs Nice to Have Experience with autonomous driving frameworks (Autoware, Apollo) — Autoware contributors especially welcome Point cloud processing with PCL or Open3D GPU optimization (CUDA) or Edge AI deployment (NVIDIA Jetson/Orin) Track record of deploying algorithms on physical robots or vehicles (Sim-to-Real) Experience with dynamic path planning (Hybrid A*, TEB, DWA) Docker + Git + CI/CD workflow experience Experience with FAST-LIO2, LIO-SAM, or other LiDAR-inertial odometry systems Tech Stack Languages: C++ (required), Python Frameworks: ROS/ROS2, PCL, OpenCV, Eigen, Ceres / g2o Tools: Linux, Docker, Git, NVIDIA CUDA (preferred) Who Will Thrive Here Thrive in an early-stage startup where you help define how things work Want direct, hands-on access to real robots — a level of freedom rare at larger companies Move fast, iterate quickly, and care about shipping things that work in the field Excited about Japan's first robotics OS platform and its real-world deployments (hospital, Tsukuba PoC)