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SLAM & Navigation Engineer

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Screenshot-2026-06-25-at-16-01-39

About Omakase Robotics

Aming to be the world’s leading humanoid company, we are building highly reliable robots for everyday real-world use, engineered with rigorous mass-production discipline. Three vertically integrated components: **Omakase D1** (our own hardware), **Omakase Zen** (the manipulation intelligence foundation), and **Omakase OS** (the orchestration software that runs robots in the field — this role).

About this Role

Our robots navigate hospitals, hotels, and retail floors — narrow corridors, glass walls, crowds, carts, and lighting that changes by the hour. You will own localization, mapping, and navigation inside Omakase OS: one engineer, full ownership, deployed robots. This role absorbs what was previously posted as two overlapping SLAM positions; we want one strong owner, not a SLAM department.

What You'll Do

- Own the localization/mapping stack end to end (we build on LiDAR-inertial odometry, e.g. FAST-LIO-class systems) for indoor human-shared environments
- Build robust state estimation: sensor fusion across LiDAR, cameras, IMU, and wheel odometry; degeneracy handling; relocalization
- Develop and tune path planning and obstacle avoidance for dynamic indoor spaces (people are not static obstacles)
- Build the mapping/calibration toolchain used at every customer-site deployment (fast site bring-up is a product feature)
- Optimize for edge compute (Jetson-class): CPU/GPU budgets shared with policy inference
- Validate in simulation and on robots; define navigation acceptance tests FDEs can run at deployment time

Required Qualifications

- **3+ years hands-on SLAM / VIO / LiDAR-inertial odometry experience with real sensor data on real platforms** (research or production; simulation-only does not qualify)
- Production-grade modern C++ and solid Python
- Strong foundations in 3D geometry, state estimation (EKF/UKF, factor graphs), and nonlinear optimization
- ROS / ROS 2 on Linux; you have debugged bad odometry at a real site before
- Point cloud processing (PCL / Open3D) and multi-sensor calibration experience

Nice to Have

- FAST-LIO / LIO-SAM family internals; loop closure and map management at building scale
- Deployment on embedded GPUs (Jetson), CUDA optimization
- Navigation among dense pedestrians; social navigation literature awareness
- Docker, CI/CD; Japanese language is a plus for site visits

Why this role

- Full ownership of a stack that ships: your maps and planners run every day at customer sites, and deployment speed you build becomes company margin.
- You sit next to the manipulation and runtime teams — navigation is integrated, not siloed.

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We kindly ask for your current and expected compensation to ensure alignment as we move forward in the process.

(※This information will not be shared with hiring managers and will not affect screening.)

 

選考を進めるにあたり、報酬条件のすり合わせのため、現在の年収およびご希望年収についてお伺いしております。

※本情報は採用担当者のみが確認し、採用マネージャーには共有されません。また、選考結果に直接影響するものではありません。

This information will help us ensure that we're aligned with your compensation expectations as we move forward in the process.

(※This information will not be shared with hiring managers and will not affect screening.)

 

選考を進めるにあたり、報酬条件のすり合わせのため、現在の年収およびご希望年収についてお伺いしております。

※本情報は採用担当者のみが確認し、採用マネージャーには共有されません。また、選考結果に直接影響するものではありません。