Forward Deployed Robotics Engineer

Role at a Glance

We are seeking a highly skilled Robotics Engineer specializing in underwater sensor fusion and localization to lead the development and deployment of robust navigation systems for our hull-cleaning and NDT inspection robots. The ideal candidate will have deep expertise in fusing data from underwater sensor suites - including IMUs, DVLs, pressure sensors, acoustic positioning systems such as USBL/LBL, and sonar-based relative sensing - to produce accurate and reliable state estimates in GPS-denied, close-to-structure environments. You will be responsible for solving real localization problems in the field: compensating for drift, handling intermittent or delayed aiding, managing sensor dropouts and acoustic outliers, and delivering pragmatic solutions that can be deployed under tight timelines.

To be successful in this role, you must possess strong hands-on experience designing and implementing advanced filtering algorithms for real-world robotic systems, such as error-state Extended Kalman Filters (EKF), Unscented Kalman Filters (UKF), and Particle Filters where appropriate. You should be adept at deriving and maintaining both linear and nonlinear state-space models to continuously estimate critical variables such as position, velocity, attitude, and sensor biases in challenging underwater conditions. Additionally, the role requires expertise in calibrating and validating real sensor stacks - including IMU bias estimation, DVL alignment and dropout handling, pressure-depth offsets, acoustic latency, and timing synchronization - as well as a strong understanding of underwater navigation concepts such as dead reckoning, inertial navigation, observability in constrained motion, degraded-mode localization, and robust fusion when absolute aids are sparse or unreliable.

Required Skills and Expertise:

• Advanced Filtering Algorithms: Proficiency designing and implementing localization and navigation filters, including Kalman Filters, error-state Extended Kalman Filters (EKF), Unscented Kalman Filters (UKF), and Particle Filters when warranted by the sensing environment or failure modes.

• Underwater Multi-Sensor Integration: Experience fusing data from complementary and redundant underwater sensors, including IMUs, DVLs, pressure sensors, USBL/LBL systems, sonar-derived relative measurements, and other non-vision localization inputs to improve robustness and reliability.

• State-Space Modeling & Observability: Strong background in formulating continuous-time and discrete-time dynamic models for underwater vehicles, including position, linear/angular velocity, attitude, sensor biases, and other latent states, with an understanding of observability under constrained motion and close-proximity operations.

• Sensor Calibration, Timing & Noise Management: Expertise in managing real-world uncertainties such as drifting IMU biases, DVL misalignment or bottom-lock loss, pressure sensor offsets, acoustic multipath and latency, clock synchronization issues, and magnetic disturbances near large steel structures.

• Underwater Navigation: Thorough understanding of GPS-denied underwater localization concepts, including dead reckoning, inertial navigation, acoustic aiding, hull-relative localization, and fallback behavior when absolute position updates are unavailable or degraded.

• Forward-Deployed Problem Solving: Demonstrated ability to deploy working sensor fusion solutions within tight timelines, debug failures from field logs and replay tools, tune estimators under operational pressure, and make practical engineering tradeoffs to keep systems moving forward.

• Fusion System Design: Familiarity designing resilient localization architectures that can tolerate delayed and asynchronous measurements, intermittent aiding, sensor dropouts, and compute or bandwidth constraints common in deployed underwater robotic systems.