{"schemaVersion":"jobsearcher.job.v1","id":"a668bdf1dedde7acdf7d6592","url":"https://jobsearcher.com/jobs/a668bdf1dedde7acdf7d6592","canonicalUrl":"https://jobsearcher.com/jobs/a668bdf1dedde7acdf7d6592","title":"Staff Deep Learning Engineer","description":"Quidient is a deep tech AI company pioneering advancements in Generalized (5D) Scene Reconstruction (GSR). GSR is poised to become one of the world's great digital product categories (think GPS, MRI, and LMM). Our flagship GSR product, Quidient Reality®, is a powerful API that enables anyone with a mobile device to virtualize, visualize, and measure anything. Words relevant to Quidient include Generative AI, Physics-Informed AI, Large Scene Models (LSMs), Large World Models (LWMs), and API-First.\nOverview\nWe are seeking a Staff Deep Learning Research Engineer to design, build, and train novel neural network architectures that solve hard problems across Quidient's GSR platform. This is not an applied-ML role — you will work from foundational principles to create new networks from scratch, implement cutting-edge papers, and run end-to-end experiments across domains including geometric anomaly detection, neural rendering, and 3D reconstruction quality.\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 Do\nResearch & Network Design\nDesign and train novel deep neural network architectures from scratch for a variety of reconstruction tasks — including surface anomaly detection (e.g., dent detection), geometry-based defect identification, and neural rendering improvements.\nImplement state-of-the-art papers and adapt published architectures to Quidient's specific reconstruction challenges, exercising deep judgment about what will translate from benchmark to production.\nIdentify technical gaps in the current reconstruction pipeline, propose neural network-based solutions, and build the roadmap for how deep learning capabilities evolve across the platform.\nDesign and maintain rigorous evaluation pipelines grounded in real-world captures to measure model performance, regression, and generalization.\nModel Development\nRun end-to-end experiments independently — from hypothesis through data preparation, training, evaluation, and iteration — with minimal supervision.\nStay current with the latest advances in deep neural network architectures, training techniques, and optimization methods, continuously bringing relevant ideas into the pipeline.\nContribute production-quality C++ and Python to integrate trained models into the reconstruction engine.\nBridge deep learning methods with the geometric and physical foundations of the reconstruction platform, applying domain expertise in one or more of: light transport, 3D reconstruction, or SLAM\nDrive inference optimization and GPU/CUDA performance work toward real-time and on-device targets.\nWhat You Bring\nMust-Have Qualifications:\nMaster's or PhD in Computer Science, Electrical Engineering, Machine Learning, or a related field. A graduate-level foundation in deep learning theory is required, not just applied experience.\n6+ years of experience in deep learning research and engineering, with demonstrated ability to design, train, and evaluate novel neural network architectures from scratch.\nDeep domain expertise in at least one of: light transport, deep learning for 3D vision, or SLAM.\nAbility to read, critically evaluate, and implement current deep learning papers (CVPR, NeurIPS, ICLR, ICML) and translate them into working systems.\nStrong software engineering in C++ and Python, with deep proficiency in PyTorch or equivalent frameworks for model development and training.\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.\nPublications or open-source contributions in deep learning, neural rendering, 3D reconstruction, or computer vision (CVPR, NeurIPS, ICLR, ICML, SIGGRAPH, or similar).\nExperience designing evaluation pipelines and experiment infrastructure for deep learning research.\nHands on with geometric or physics-informed neural networks, or anomaly detection in 3D data.\nTrack record of taking a research idea from paper to production-deployed model.\nWhat We Offer\nCompensation:\nSalary Range: $185,000 – $235,000.\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.","company":"Quidient","rawCompany":"quidient","city":"Baltimore","state":"MD","isRemote":false,"isActive":false,"createdAt":"2026-08-03T22:20:07.413Z","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":"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"},{"code":"541330","title":"Engineering Services","slug":"engineering-services"},{"code":"541690","title":"Other Scientific and Technical Consulting Services","slug":"other-scientific-and-technical-consulting-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Staff Deep Learning Engineer","description":"Quidient is a deep tech AI company pioneering advancements in Generalized (5D) Scene Reconstruction (GSR). GSR is poised to become one of the world's great digital product categories (think GPS, MRI, and LMM). Our flagship GSR product, Quidient Reality®, is a powerful API that enables anyone with a mobile device to virtualize, visualize, and measure anything. Words relevant to Quidient include Generative AI, Physics-Informed AI, Large Scene Models (LSMs), Large World Models (LWMs), and API-First.\nOverview\nWe are seeking a Staff Deep Learning Research Engineer to design, build, and train novel neural network architectures that solve hard problems across Quidient's GSR platform. This is not an applied-ML role — you will work from foundational principles to create new networks from scratch, implement cutting-edge papers, and run end-to-end experiments across domains including geometric anomaly detection, neural rendering, and 3D reconstruction quality.\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 Do\nResearch & Network Design\nDesign and train novel deep neural network architectures from scratch for a variety of reconstruction tasks — including surface anomaly detection (e.g., dent detection), geometry-based defect identification, and neural rendering improvements.\nImplement state-of-the-art papers and adapt published architectures to Quidient's specific reconstruction challenges, exercising deep judgment about what will translate from benchmark to production.\nIdentify technical gaps in the current reconstruction pipeline, propose neural network-based solutions, and build the roadmap for how deep learning capabilities evolve across the platform.\nDesign and maintain rigorous evaluation pipelines grounded in real-world captures to measure model performance, regression, and generalization.\nModel Development\nRun end-to-end experiments independently — from hypothesis through data preparation, training, evaluation, and iteration — with minimal supervision.\nStay current with the latest advances in deep neural network architectures, training techniques, and optimization methods, continuously bringing relevant ideas into the pipeline.\nContribute production-quality C++ and Python to integrate trained models into the reconstruction engine.\nBridge deep learning methods with the geometric and physical foundations of the reconstruction platform, applying domain expertise in one or more of: light transport, 3D reconstruction, or SLAM\nDrive inference optimization and GPU/CUDA performance work toward real-time and on-device targets.\nWhat You Bring\nMust-Have Qualifications:\nMaster's or PhD in Computer Science, Electrical Engineering, Machine Learning, or a related field. A graduate-level foundation in deep learning theory is required, not just applied experience.\n6+ years of experience in deep learning research and engineering, with demonstrated ability to design, train, and evaluate novel neural network architectures from scratch.\nDeep domain expertise in at least one of: light transport, deep learning for 3D vision, or SLAM.\nAbility to read, critically evaluate, and implement current deep learning papers (CVPR, NeurIPS, ICLR, ICML) and translate them into working systems.\nStrong software engineering in C++ and Python, with deep proficiency in PyTorch or equivalent frameworks for model development and training.\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.\nPublications or open-source contributions in deep learning, neural rendering, 3D reconstruction, or computer vision (CVPR, NeurIPS, ICLR, ICML, SIGGRAPH, or similar).\nExperience designing evaluation pipelines and experiment infrastructure for deep learning research.\nHands on with geometric or physics-informed neural networks, or anomaly detection in 3D data.\nTrack record of taking a research idea from paper to production-deployed model.\nWhat We Offer\nCompensation:\nSalary Range: $185,000 – $235,000.\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.","datePosted":"2026-08-03T22:20:07.413Z","dateModified":"2026-08-03T22:20:07.413Z","hiringOrganization":{"@type":"Organization","name":"Quidient","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Baltimore","addressRegion":"MD","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"a668bdf1dedde7acdf7d6592"},"url":"https://jobsearcher.com/jobs/a668bdf1dedde7acdf7d6592"}}