CI
Research Scientist, SLAM & VIO
Accepting applicationsCHEManager International · New York, NY
Full-Time Mid AIC++Pythonaiate
Posted
1d ago
Category
Test
Experience
Mid
Country
United States
About Mecka AI
Mecka AI is building the data infrastructure layer for robotics and embodied AI.
We partner with leading AI labs and robotics companies to deliver high-quality, real-world datasets used to train, evaluate, and deploy robotic systems - where model performance is dictated by data quality.
The Role
We 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 On
Monocular 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 Are
Required 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.
Show more Show less
Mecka AI is building the data infrastructure layer for robotics and embodied AI.
We partner with leading AI labs and robotics companies to deliver high-quality, real-world datasets used to train, evaluate, and deploy robotic systems - where model performance is dictated by data quality.
The Role
We 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 On
Monocular 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 Are
Required 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.
Show more Show less