Responsibilities

Design, prototype, evaluate, and productionize advanced computer vision and deep learning systems for 3D reconstruction, scene understanding, semantic modeling, and structured property representation
Work across the full lifecycle of applied research and engineering: identifying relevant academic and industry approaches, writing project plans and technical specs, building proof-of-concepts, training and evaluating models, analyzing reconstruction quality and performance, and integrating successful approaches into production systems
Partner closely with 3D reconstruction, Product, Design, Engineering, modeling, infrastructure, graphics, frontend, and backend teams to turn ambiguous technical and customer needs into clearly scoped experiments and production capabilities
Work may include: mobile capture, aerial imagery, multimodal sensor fusion, multi-view reconstruction, pose estimation, feature matching, dense geometry, model fitting, semantic understanding, volumetric/surfel/surface-based representations, CAD-quality structured outputs, Gaussian Splatting, VLMs, or foundation-model-style approaches for correspondence and 3D understanding

Requirements

5+ years of experience in computer vision, machine learning, or 3D reconstruction within academic research and/or industry environments
Hands-on experience with one or more areas of modern 3D vision and reconstruction (multi-view geometry, pose estimation, camera calibration, structure-from-motion, feature matching, dense reconstruction, structured 3D reconstruction, model fitting, optimization, scene understanding, semantic modeling, or structured 3D representation)
Practical deep learning experience applied to 3D reconstruction, geometry, correspondence, pose, segmentation, semantic understanding, VLMs, or related spatial ML problems
Strong software engineering skills in Python and/or C++
Ability to prototype quickly, train models, evaluate approaches rigorously, and translate promising research into practical systems
Experience designing experiments with clear metrics, baselines, datasets, evaluation plans, and go/no-go criteria
Ability to write clear project plans, technical specs, research summaries, experiment reports, and production-readiness documentation
Track record of technical ownership and the ability to independently drive projects from ambiguity through implementation and delivery
Strong collaboration skills working with researchers, engineers, 3D modelers, infrastructure teams, product partners, and cross-functional stakeholders
Strong product and engineering judgment balancing technical ambition, customer value, production constraints, scalability, reliability, timing, and business impact
Master's or PhD in Computer Science, Machine Learning, Computer Vision, Robotics, Graphics, or a related field

Staff-Level Additional Requirements

Experience building and shipping production CV, ML, or 3D reconstruction systems at scale
Ability to define technical direction, architectural strategy, evaluation methodology, and quality/performance standards for complex CV or ML systems
Proven ability to lead initiatives from early-stage research and experimentation through production deployment, monitoring, and long-term system ownership
Ability to operate effectively in ambiguous problem spaces and drive alignment across cross-functional stakeholders
Strong communication and influence skills translating complex technical concepts for both technical and non-technical audiences
Experience mentoring engineers and raising the technical bar through architecture reviews, technical leadership, experimentation frameworks, and engineering best practices
Track record of translating research or technical innovation into measurable product, customer, or business impact