Senior Applied Research Engineer, 3D Computer Vision

Responsibilities

• 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 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.

Qualifications

• 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.