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)