Senior Applied Research Engineer, 3D Computer Vision (USA)

• Design, train, evaluate, and ship 3D reconstruction systems across our pipeline — gaussian splats, foundation 3D models, SfM, MVS, monocular depth, mesh reconstruction. • Drive integration of modern reality capture approaches (splatting, foundation models) into our production stack — making the calls on what's ready to ship and what isn't. • Own the hardest technical investigations in your area, from initial triage through production rollout and long-tail support. • Optimize 3D systems for speed, accuracy, and efficiency at production scale. • Use the right tool for the problem — classical 3D computer vision when it wins, learned approaches when they win. • Stay current with 3D vision research and evaluate promising techniques against our workflows. • Hold a high technical bar for your own work — high-quality designs, well-tested code, production-ready ship habits. • Contribute to the team through code review, pairing, and design feedback. • Codify debugging and investigation playbooks into reusable skills. • Use AI tools daily across the SDLC, with judgment on where they help and where they don't. • Author agent skills or tooling that other engineers use; contribute to the team's shared skills library. • Conduct rigorous evaluations of new AI tools and bring useful patterns to the team. • Review agent-generated code with the same rigor as human-written code. • Track the AI tooling landscape and bring useful patterns to the team.

• Bachelor's, Master's, or PhD in Computer Science, Engineering, or a related field, with 5+ years of professional experience in 3D Computer Vision or 3D Machine Learning, or a PhD in Computer Vision, Machine Learning, or a closely related discipline. • Hands-on experience with modern reality capture — specifically, gaussian splatting and/or foundation 3D reconstruction models (MASt3R, VGGT, or comparable). At least one of these from real hands-on work (production, startup, or other industry experience), not just paper familiarity. • Demonstrable track record of building and shipping 3D reconstruction systems — in production, at a startup, or in another applied industry setting (not just research or prototype work). • Deep experience across the broader 3D perception toolkit — at least three of: feature detection and matching, SfM, MVS, monocular depth estimation, mesh reconstruction, SLAM. Published research or open-source contributions in any of these areas is a plus. • Comfort across the classical-vs-learned spectrum — you reach for the right tool, not the trendiest one. • A real track record with agentic development. • Strong C++ proficiency — much of our photogrammetry pipeline runs in C++ and you'll be working in it daily. • Strong ability to timebox experiments, iterate effectively, and triage routes to success when the path isn't obvious. • Fluency in modern ML frameworks (PyTorch, TensorFlow, or equivalent) and modern training stacks. • Ability to work as an effective remote engineer with AM standup overlap with PST. • Strong written and verbal communication; you can take a technically dense investigation and make it land with PMs, leadership, and other engineers. • Open-source agent skills, plugins, or prompts that others use. • Experience running and monitoring many concurrent ML experiments in cloud environments. • Comfort with cloud training and inference (AWS, GCP, Azure) and containerization (Docker, Kubernetes). • Experience with geospatial systems or aerial imagery pipelines.