{"schemaVersion":"jobsearcher.job.v1","id":"e38c0ef6129bf67b4d3fea0a","url":"https://jobsearcher.com/jobs/e38c0ef6129bf67b4d3fea0a","canonicalUrl":"https://jobsearcher.com/jobs/e38c0ef6129bf67b4d3fea0a","title":"Offline Mapping Engineer","description":"Overview\nQuidient is seeking a Senior Offline Mapping Engineer to own and advance the accuracy and robustness of our offline mapping and 3D reconstruction systems. This role sits at the core of our Generalized Scene Reconstruction Platform, where classical SfM and multi-view stereo provide the foundation and modern deep learning methods push quality over the edge — particularly in textureless, featureless, and highly reflective environments that defeat traditional approaches. You will enhancethe pipeline that turns real-world captures into highly accurate, production-quality 3D reconstructions.\nThis is a hybrid position, meaning that you will need to live within easy driving distance to our Technology Center in Columbia, Maryland.\nWhat You’ll DoOffline Mapping & Reconstruction\nDevelop 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.\nReduce pose estimation failures and increase geometric accuracy in environments where classical methods degrade, using a combination of improved estimation and learned components.\nDesign 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.\nImprove calibration pipelines, bundle adjustment robustness, and dense reconstruction fidelity in offline processing contexts where throughput matters but hard real-time does not.\nResearch Integration & Evaluation\nBuild and maintain evaluation methodology grounded in real-world captures — covering feature-rich, textureless, and reflective environments — not synthetic benchmarks alone.\nStay current with the deep learning and 3D vision literature, applying good judgment about which methods are production-viable and which are benchmark artifacts.\nCollaborate closely with the SLAM and real-time mapping team to share components and ensure offline improvements feed back into the broader reconstruction platform.\nWhat You BringMust-Have Qualifications:\nMaster’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.\nSignificant hands-on experience building or substantially improving an offline SfM, multi-view stereo, or dense reconstruction system in production — not just research prototypes.\nStrong C++ and Python.\nDeep working knowledge of multiview geometry, bundle adjustment, nonlinear estimation, and — critically — the practical failure modes of each.\nReal experience with sensor calibration on real hardware.\nHands-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.\nWillingness to work on-site in Columbia, MD, in a hybrid capacity.\nMeet 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.\nNice-to-Have Qualifications:\nExperience in fast-paced or startup environments.\nPrior work on reconstruction of textureless, reflective, or geometrically challenging environments.\nPublished or shipped work combining learned and classical methods in 3D vision pipelines.\nExpertise in neural scene representations (NeRF, Gaussian Splatting, or similar).\nExperience with large-scale numerical optimization.\nContributor to open-source SfM, MVS, or 3D reconstruction projects (COLMAP, OpenMVS, or similar).\nTrack record of shipping mapping or reconstruction systems at production scale.\nWhat We OfferCompensation:\nSalary Range: $175,000 - $230,000\nAnnual bonus and equity as appropriate.\nBenefits:\nHealth insurance\nHSA\n401(k) with company match\nLife & disability insurance\nPaid holidays & generous PTO\nOpportunities for bonuses, equity, and career growth\nEqual Opportunity Employer Statement\nQuidient is an Equal Opportunity Employer. Quidient will consider all qualified applicants without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, veteran status, disability, or any other classification protected by applicable state, federal, or local laws.\nPay: $175,000.00 - $230,000.00 per year\nBenefits:\n401(k)\n401(k) matching\nDental insurance\nFlexible schedule\nHealth insurance\nHealth savings account\nLife insurance\nPaid parental leave\nPaid time off\nParental leave\nProfessional development assistance\nRelocation assistance\nStock options\nTuition reimbursement\nVision insurance\nApplication Question(s):\nWill you now or in the future require Visa sponsorship?\nDo you have significant hands-on experience building or substantially improving an offline SfM, multi-view stereo, or dense reconstruction system?\nDo you have deep working knowledge of multiview geometry, bundle adjustment, nonlinear estimation, and the practical failure modes of each?\nWork Location: Hybrid remote in Columbia, MD 21046","company":"Quidient","rawCompany":"quidient","city":"Columbia","state":"MD","isRemote":false,"isActive":false,"createdAt":"2026-08-06T15:43:26.669Z","occupations":[{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"541370","title":"Surveying and Mapping (except Geophysical) Services","slug":"surveying-and-mapping-except-geophysical-services"},{"code":"541360","title":"Geophysical Surveying and Mapping Services","slug":"geophysical-surveying-and-mapping-services"},{"code":"541715","title":"Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)","slug":"research-and-development-in-the-physical-engineering-and-life-sciences-except-nanotechnology-and-biotechnology"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Offline Mapping Engineer","description":"Overview\nQuidient is seeking a Senior Offline Mapping Engineer to own and advance the accuracy and robustness of our offline mapping and 3D reconstruction systems. This role sits at the core of our Generalized Scene Reconstruction Platform, where classical SfM and multi-view stereo provide the foundation and modern deep learning methods push quality over the edge — particularly in textureless, featureless, and highly reflective environments that defeat traditional approaches. You will enhancethe pipeline that turns real-world captures into highly accurate, production-quality 3D reconstructions.\nThis is a hybrid position, meaning that you will need to live within easy driving distance to our Technology Center in Columbia, Maryland.\nWhat You’ll DoOffline Mapping & Reconstruction\nDevelop 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.\nReduce pose estimation failures and increase geometric accuracy in environments where classical methods degrade, using a combination of improved estimation and learned components.\nDesign 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.\nImprove calibration pipelines, bundle adjustment robustness, and dense reconstruction fidelity in offline processing contexts where throughput matters but hard real-time does not.\nResearch Integration & Evaluation\nBuild and maintain evaluation methodology grounded in real-world captures — covering feature-rich, textureless, and reflective environments — not synthetic benchmarks alone.\nStay current with the deep learning and 3D vision literature, applying good judgment about which methods are production-viable and which are benchmark artifacts.\nCollaborate closely with the SLAM and real-time mapping team to share components and ensure offline improvements feed back into the broader reconstruction platform.\nWhat You BringMust-Have Qualifications:\nMaster’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.\nSignificant hands-on experience building or substantially improving an offline SfM, multi-view stereo, or dense reconstruction system in production — not just research prototypes.\nStrong C++ and Python.\nDeep working knowledge of multiview geometry, bundle adjustment, nonlinear estimation, and — critically — the practical failure modes of each.\nReal experience with sensor calibration on real hardware.\nHands-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.\nWillingness to work on-site in Columbia, MD, in a hybrid capacity.\nMeet 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.\nNice-to-Have Qualifications:\nExperience in fast-paced or startup environments.\nPrior work on reconstruction of textureless, reflective, or geometrically challenging environments.\nPublished or shipped work combining learned and classical methods in 3D vision pipelines.\nExpertise in neural scene representations (NeRF, Gaussian Splatting, or similar).\nExperience with large-scale numerical optimization.\nContributor to open-source SfM, MVS, or 3D reconstruction projects (COLMAP, OpenMVS, or similar).\nTrack record of shipping mapping or reconstruction systems at production scale.\nWhat We OfferCompensation:\nSalary Range: $175,000 - $230,000\nAnnual bonus and equity as appropriate.\nBenefits:\nHealth insurance\nHSA\n401(k) with company match\nLife & disability insurance\nPaid holidays & generous PTO\nOpportunities for bonuses, equity, and career growth\nEqual Opportunity Employer Statement\nQuidient is an Equal Opportunity Employer. Quidient will consider all qualified applicants without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, veteran status, disability, or any other classification protected by applicable state, federal, or local laws.\nPay: $175,000.00 - $230,000.00 per year\nBenefits:\n401(k)\n401(k) matching\nDental insurance\nFlexible schedule\nHealth insurance\nHealth savings account\nLife insurance\nPaid parental leave\nPaid time off\nParental leave\nProfessional development assistance\nRelocation assistance\nStock options\nTuition reimbursement\nVision insurance\nApplication Question(s):\nWill you now or in the future require Visa sponsorship?\nDo you have significant hands-on experience building or substantially improving an offline SfM, multi-view stereo, or dense reconstruction system?\nDo you have deep working knowledge of multiview geometry, bundle adjustment, nonlinear estimation, and the practical failure modes of each?\nWork Location: Hybrid remote in Columbia, MD 21046","datePosted":"2026-08-06T15:43:26.669Z","dateModified":"2026-08-06T15:43:26.669Z","hiringOrganization":{"@type":"Organization","name":"Quidient","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Columbia","addressRegion":"MD","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"e38c0ef6129bf67b4d3fea0a"},"url":"https://jobsearcher.com/jobs/e38c0ef6129bf67b4d3fea0a"}}