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)