Mapping & Localization Engineer at FieldAI

Irvine, California, United States -

Full Time

Start Date

Immediate

Expiry Date

22 Aug, 26

Salary

180000.0

Posted On

24 May, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

SLAM, Localization, ICP/GICP/NDT Registration, Point Cloud Processing, Modern C++, Geometric Optimization, Scan Matching, Robotics Middleware, PCL (Point Cloud Library), LiDAR Mapping

Industry

Robotics Engineering

Description

FieldAI’s Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California’s robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence. If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field. FieldAI is seeking a Mapping & Localization Engineer to help improve mapping quality, localization accuracy, and scan registration capabilities across our robotics and mapping platform. This role focuses on: SLAM and localization workflows ICP/GICP/NDT registration pipelines point cloud processing scan stitching accuracy geometric optimization mapping robustness and operational reliability You’ll work closely with mapping, robotics integration, and deployment teams to improve mapping performance across large-scale facility scanning and robotics-enabled workflows. \n What You'll Do Mapping & Localization Improve ICP/GICP/NDT registration workflows Support SLAM and localization pipelines across mapping systems Improve scan stitching accuracy and mapping robustness Optimize point cloud registration and alignment workflows Improve localization performance across large-scale operational environments Point Cloud Processing & Optimization Improve point cloud processing performance and scalability Support geometric optimization and scan matching workflows Debug and improve existing mapping and localization systems Contribute to mapping quality and operational reliability improvements Cross-Functional Collaboration Partner closely with mapping, robotics, and deployment teams Support downstream processing and operational deployment workflows Help improve deployment readiness and mapping scalability Contribute to debugging and operational problem-solving across mapping systems What You Bring Bachelor’s or Master’s degree in Computer Science, Robotics, Electrical Engineering, Mechanical Engineering, or a related technical field 3–5 years of industry or applied robotics experience Strong experience with SLAM and localization systems Experience with ICP/GICP/NDT registration techniques Strong understanding of point cloud registration and scan matching Strong modern C++ engineering skills Experience working within production robotics or mapping environments Strong debugging and systems-thinking capability Ability to operate effectively in fast-moving startup environments Ownership mentality with strong execution and problem-solving instincts What Sets You Apart Experience with LiDAR-based mapping systems Experience with robotics middleware Background in robotics, autonomy, industrial scanning, or localization systems Familiarity with PCL (Point Cloud Library) Experience balancing research ideas with production engineering realities Strong operational and deployment-oriented mindset \n $140,000 - $180,000 a year \n Our salary range is generous and we consider each individual’s background and experience when determining final compensation. Base pay may vary based on role scope, job-related knowledge, skills, experience, and the Irvine, California market. Why Join FieldAI in Irvine? In Irvine, you will work where the robots are. Our local team builds and tests systems on real hardware with real sensors, then ships them to operate in unstructured, previously unknown environments around the world. We are solving one of robotics’ hardest challenges: reliable deployment outside the lab. Our Field Foundational Models™ raise the bar for perception, planning, localization, and manipulation, with an emphasis on explainability and safety for real-world use. You will collaborate with a world-class team that thrives on creativity, resilience, and bold thinking. We bring deep experience from organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise, Zoox, Toyota Research Institute, and SpaceX, along with a track record of field deployments and strong performance in DARPA challenge segments. Be Part of the Next Robotics Revolution We are looking for builders who want their work to leave the whiteboard and show up on robots. If you enjoy tackling tough, uncharted questions and working across disciplines, you will find your people here. Our teams span AI, software, robotics engineering, product, field deployment, and technical communication, all focused on shipping systems that perform in the real world. Our headquarters is in Irvine, and we partner closely with teams there as well as colleagues across the US and around the world. Join us in Southern California and help define what dependable, field-ready autonomy looks like. We value diverse perspectives and are committed to fostering an inclusive workplace. We evaluate candidates and employees based on merit, qualifications, and performance, and we do not discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, or any other legally protected statu

Responsibilities

The engineer will improve mapping quality, localization accuracy, and scan registration pipelines for robotics platforms. This includes optimizing point cloud processing and collaborating with cross-functional teams to ensure operational reliability in large-scale environments.