Senior Offline Mapping Engineer

Offline Mapping & Reconstruction

• Develop and advance our offline mapping and reconstruction pipeline, driving accuracy and robustness across the hardest capture scenarios — textureless walls, highly reflective surfaces, featureless geometry, and large-scale scenes.
• Reduce pose estimation failures and increase geometric accuracy in environments where classical methods degrade, using a combination of improved estimation and learned components.
• Design and integrate deep learning methods (learned feature matching, monocular depth priors, learned outlier rejection) alongside classical SfM and MVS components to close the last 10% of reconstruction quality.
• Improve calibration pipelines, bundle adjustment robustness, and dense reconstruction fidelity in offline processing contexts where throughput matters but hard real-time does not.

Research Integration & Evaluation

• Build and maintain evaluation methodology grounded in real-world captures — covering feature-rich, textureless, and reflective environments — not synthetic benchmarks alone.
• Stay current with the deep learning and 3D vision literature, applying good judgment about which methods are production-viable and which are benchmark artifacts.
• Collaborate closely with the SLAM and real-time mapping team to share components and ensure offline improvements feed back into the broader reconstruction platform.

Must-Have Qualifications

• Master’s, or PhD in Computer Science, Computer Vision, Robotics, or a related field — or equivalent demonstrated experience. This is a Senior-to-Staff level role.
• Significant hands-on experience building or substantially improving an offline SfM, multi-view stereo, or dense reconstruction system in production — not just research prototypes.
• Strong C++ and Python.
• Deep working knowledge of multiview geometry, bundle adjustment, nonlinear estimation, and — critically — the practical failure modes of each.
• Real experience with sensor calibration on real hardware.
• Hands-on ability to design, train, and integrate deep learning components (learned matching, depth estimation, feature extraction) into a classical reconstruction pipeline using PyTorch or equivalent.
• Willingness to work on-site in Columbia, MD, in a hybrid capacity.
• Meet Quidient, customer, and government security requirements, which may include, but are not limited to a background check, citizenship verification, and Criminal Justice Information Services verification.

Nice-to-Have Qualifications

• Experience in fast-paced or startup environments.
• Prior work on reconstruction of textureless, reflective, or geometrically challenging environments.
• Published or shipped work combining learned and classical methods in 3D vision pipelines.
• Expertise in neural scene representations (NeRF, Gaussian Splatting, or similar).
• Experience with large-scale numerical optimization.
• Contributor to open-source SfM, MVS, or 3D reconstruction projects (COLMAP, OpenMVS, or similar).
• Track record of shipping mapping or reconstruction systems at production scale.

Compensation & Benefits

Salary Range: $175,000 - $230,000
Annual bonus and equity as appropriate.

Benefits:

• Health insurance
• HSA
• 401(k) with company match
• Life & disability insurance
• Paid holidays & generous PTO
• Opportunities for bonuses, equity, and career growth