The RoleWe are hiring a
Research Scientist (SLAM & Visual-Inertial Odometry) to build and validate state estimation systems that work in the real world, on messy sensors, under tight compute and reliability constraints.
This role is research-heavy but production-minded: you will ship algorithms that survive scale, long runtimes, and operational edge cases.
What You'll Work OnMonocular Visual(-Inertial) Odometry (Online)- Develop robust monocular VO/VIO pipelines (feature-based and/or learned) with strong failure detection and recovery.
- Address scale ambiguity with inertial fusion, motion priors, and consistency constraints.
- Online performance: low latency, bounded memory, and stable tracking across lighting, motion blur, rolling shutter, and dynamic objects.
Monocular SLAM (Offline / Batch)- Build offline reconstruction pipelines for long trajectories: global BA, loop closure at scale, and map optimization.
- Produce high-quality trajectories and sparse/dense maps for downstream data products (labeling, QA, training signals).
- Design evaluation tooling: drift decomposition, per-segment error, and systematic bias detection.
Stereo Visual(-Inertial) Odometry (Online)- Implement stereo VO/VIO with accurate calibration handling (intrinsics/extrinsics, temporal sync) and robust matching.
- Improve depth reliability across challenging scenes (low texture, repetitive patterns, specularities).
- Optimize for stability and long-duration runs: track health metrics, relocalization, and graceful degradation.
Stereo SLAM (Offline / Batch)- Large-scale mapping and trajectory refinement using stereo constraints.
- Loop closure + global pose graph optimization with principled uncertainty handling.
- Produce maps that are useful, not just pretty: consistent frames, repeatable landmarks, and clear quality scores.
Common Themes (Monocular + Stereo)- Sensor modeling & calibration: rolling shutter, time offsets, IMU noise/scale factors, and temperature-driven drift.
- Robustness engineering: automatic resets, outlier handling, and "what broke?" diagnostics.
- Metrics & datasets: design evaluation suites, curate failure cases, and define release gates.
Who You AreRequired Background- Strong experience in SLAM / VO / VIO (academia or industry), with evidence of shipped systems or publishable results.
- Solid understanding of estimation: nonlinear least squares, factor graphs, filtering/smoothing, and uncertainty.
- Proficiency in C++ (and comfort in Python for research and evaluation).
Strong Signals- You have built systems that run for hours/days and degrade gracefully, not just "works on a benchmark."
- You understand real sensor failure modes: calibration drift, sync issues, rolling shutter, motion blur, low light.
- Experience with modern tooling (e.g., GTSAM/Ceres), and strong intuition for optimization and numerics.
Nice to Have- Experience with learned front-ends/back-ends (e.g., learned features, depth, relocalization, or hybrid classical+ML pipelines).
- Experience building offline mapping / batch optimization pipelines for large datasets.
- Familiarity with embedded/edge constraints and profiling/optimization.
Why This Role- Work on state estimation that directly impacts real-world robotics data capture and downstream model performance.
- High ownership across research, engineering, and operations; you define the bar for "good enough to ship."
- Access to challenging real-world data and the ability to shape the dataset + tooling ecosystem around the algorithms.
