Lead Software Engineer, 3D Computer Vision (USA)

• Lead the design, evaluation, and deployment of our production photogrammetry pipeline — feature detection and matching, structure from motion, multi-view stereo, monocular depth estimation, mesh reconstruction, and the supporting infrastructure that makes them work at scale. • Drive integration and evaluation of newer 3D reconstruction approaches — gaussian splats, foundation 3D models (MASt3R / DUSt3R-class and successors), and what comes next — where they earn their place in the pipeline. • Optimize 3D systems for speed, accuracy, and robustness at production scale. • Use the right tool for the problem — classical 3D computer vision when it wins, learned approaches when they win. • Own the hardest technical investigations end-to-end — initial triage through production rollout and long-tail support. • Set the technical bar for photogrammetry on the team — review designs, raise the quality of experiments, codify what "great" looks like. • Own the production observability standard for photogrammetry — KPIs, instrumentation (metrics, logs, dashboards, alerts), and the processes that catch data-quality issues early and sustain performance at scale. • Mentor engineers on the team, then take on 1–2 direct reports in your first year and grow your scope over time as the sub-team expands. • Drive execution of the photogrammetry core roadmap in tight partnership with CV leadership. Translate strategic direction into shipped work, sequence it well, and call out when something isn't going to land in time to course-correct. • Codify debugging and investigation playbooks into reusable skills to empower the team and support the organization, minimizing escalations. • Reshape how the team uses AI across the SDLC — driving adoption of AI-first and prototype-first workflows and pushing the bar on what these tools should do for us. • Author reusable tooling that scales the team — agent skills, prompts, debugging playbooks, custom tooling — so other engineers (and our support org) can stand on your shoulders rather than escalate. • Run rigorous evaluations of AI tools — where they help, where they hurt, and how to tell. • Drive the adoption of prototype-first and AI-first development workflows. • Review agent-generated code with the same rigor as human-written code, and establish standards for trust and reliability. • Treat the AI tooling landscape as a moving target: actively track what's new, run it against current problems, and champion effective new tools across the team.

• Bachelor's, Master's, or PhD in Computer Science, Engineering, or a related field, with 7+ years of professional experience in 3D Computer Vision or 3D Machine Learning. • Deep experience with production photogrammetry or 3D reconstruction systems at scale — you've built, shipped, and operated pipelines under customer load, 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, mesh texturing, SLAM. • Comfort across the classical-vs-learned spectrum — you reach for the right tool, not the trendiest one. • A track record with agentic development (see the section above). This is a filter, not a nice-to-have. • Experience setting technical direction for a team and growing the engineers on it — formally or informally — and a clear interest in moving further into people leadership over time. • Strong ability to timebox experiments, iterate effectively, and triage routes to success when the path isn't obvious. • Experience with CI/CD tools (e.g., Jenkins, GitHub Actions). • 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. • Experience with gaussian splatting and/or foundation 3D reconstruction (MASt3R, DUSt3R, or comparable). • Proficiency in C++ for performance-critical paths. • Fluency in modern ML frameworks (PyTorch, TensorFlow, or equivalent) and modern training stacks. • Experience running and monitoring many concurrent ML experiments in cloud environments. • Comfort with cloud processing (AWS, GCP, Azure) and containerization (Docker, Kubernetes). • Experience with geospatial systems or aerial imagery pipelines.