In this role, you will design, prototype, evaluate, and productionize advanced computer vision and deep learning systems for 3D reconstruction, scene understanding, semantic modeling, and structured property representation. You will 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.
You will 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. Success in this role requires strong technical judgment, clear communication, and the ability to make practical tradeoffs between research quality, user impact, production constraints, performance, reliability, and cost.
Your 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, or surface-based representations, CAD-quality structured outputs, and emerging methods such as Gaussian Splatting, VLMs, or foundation-model-style approaches for correspondence and 3D understanding.
At the Staff level, you will also be expected to shape technical direction across larger initiatives: framing problems, sizing opportunities, prioritizing technical investments, and making “stop / iterate / scale” judgments across reconstruction approaches, model development, data strategy, quality targets, and product impact. You will communicate technical tradeoffs, resource needs, and roadmap implications to cross-functional partners and leadership.
You will help raise the technical bar for the team by setting standards for performance, reliability, maintainability, and cost; leading by example in Python and/or C++; and mentoring engineers through design reviews, architecture discussions, experiment plans, and production-readiness reviews.