We're hiring a player-coach to lead execution on our core photogrammetry pipeline — the 3D reconstruction stack at the heart of our platform. You'll partner with CV leadership on technical direction, then make it happen — driving execution, setting the technical bar, growing a team, and shipping the work that moves the product.
You'll spend most of your time hands-on — building, training, and shipping 3D systems — while also growing into a leadership role. You'll start with 1–2 direct reports in your first year and grow your scope over time. This is not a pure IC role: candidates who are not interested in growing into people leadership won't be a fit.
We're a small, high-leverage team behind an industry-leading photogrammetry platform. Our customers have mapped over 150M acres across more than 180 countries, relying on us for speed, quality, and accuracy in industries as diverse as construction, agriculture, mining, conservation, forestry, and infrastructure inspection.
What matters most is experience with production photogrammetry at scale — you've shipped and operated 3D reconstruction systems, you know what breaks under load, and you understand the gap between a research result and something that holds up across thousands of maps.
You'll also help reshape how our team builds. We've been investing heavily in agentic development — codifying expertise into reusable skills, pushing AI-first workflows, evaluating new tools as they ship. We want someone who's been doing the same thing on their current team and is ready to do more of it here.
Work Model: Remote (work from home), with a strong preference for candidates who live in or can overlap substantially with Pacific Time.
Travel: Occasional travel (approximately 2–3 times per year) for team and company offsites, technical planning sessions, and in-person collaboration.
AI tooling: Daily use of advanced AI coding agents (e.g., Cursor, Claude Code, or comparable tools) is an expected part of this role. You will be responsible for defining and refining how these tools are used on the team—authoring reusable skills and workflows, evaluating new tools against real problems, and holding agent-generated code to the same standard as human-written code.
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.
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