5+ years of experience in sensor calibration, SLAM, mapping, localization, or multi-sensor fusion
Bachelor's, Master's, or PhD in Computer Science, Robotics, EE, Mathematics, or related field
Strong software engineering skills in C++ and/or Python
Demonstrated ability to solve complex technical problems from ambiguity to deployment
Strong grasp of estimation theory, including Kalman filters and optimization-based fusion
Responsibilities
Design and implement sensor calibration systems for various sensors
Build and maintain mapping pipelines, including map creation and drift detection
Develop and improve localization algorithms, including sensor fusion and SLAM
Define accuracy, latency, and reliability requirements for outputs
Build tooling to validate calibration and localization quality at fleet scale
Diagnose and resolve calibration and localization failures from testing
Benefits
Opportunity to shape foundational technology in a growing team
Work on a diverse fleet of vehicles including passenger cars and robotaxis
Engage in cutting-edge technology development in autonomous driving
Collaborate with a founding team to establish innovative methodologies
Potential for career growth in a rapidly evolving field
Full Job Description
About the Role
ML-first does not mean ML-only: every model this team ships depends on a vehicle that knows precisely where its sensors are, where it is in the world, and how confident it can be in both. This role owns that foundation, building the calibration, mapping, and localization stack that every perception and planning model is built on top of, across a multi-vehicle-class fleet spanning passenger vehicles, robotaxis, buses, and trucks.
Founding Team Opportunity
Most autonomy programs set their calibration and localization architecture years ago and are now stuck maintaining it. This team is establishing its framework now, and an early hire will help shape:
Sensor calibration methodology and tooling, both online and offline
Mapping strategy, representation, and drift-detection pipelines
Localization architecture and the technical roadmap as the fleet scales
Responsibilities
Design and implement sensor calibration systems (camera, LiDAR, radar, IMU) for factory and in-field use
Build and maintain mapping pipelines, including map creation, versioning, and drift detection
Develop and improve localization algorithms, including sensor fusion, odometry, and SLAM
Define accuracy, latency, and reliability requirements for outputs consumed by downstream teams
Build tooling to validate calibration and localization quality at fleet scale
Diagnose and resolve calibration and localization failures from on-road and simulation testing
Qualifications
A demonstrated ability to take difficult technical problems from ambiguity to successful deployment
Bachelor's, Master's, or PhD in Computer Science, Robotics, EE, Mathematics, or related field
5+ years of experience in sensor calibration, SLAM, mapping, localization, or multi-sensor fusion
Strong grasp of estimation theory, including Kalman filters, factor graphs, or optimization-based fusion
Strong software engineering skills in C++ and/or Python, with a track record of leading projects from concept through deployment
Preferred Qualifications
Autonomous driving or robotics background across multiple vehicle classes
Experience with HD map creation and maintenance pipelines at scale
Experience with LiDAR, camera, or radar extrinsic calibration in production vehicles
Familiarity with functional safety requirements such as ISO 26262
The screening procedures may vary depending on the position, schedule, or other circumstances. You will be individually notified of the screening schedule and results via the email address provided in your application.
Compensation
$200,000 - $300,000
Additional Information
In accordance with fair hiring practices, do not include any personal information unrelated to your job qualifications (e.g., Social Security Number, family relations, marital status, age, photo, physical condition, place of birth, etc.) in your resume.
All documents must be submitted in PDF format and under 30MB in size.
If you experience issues uploading your resume, please send it along with the job posting URL to [redacted].
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