Over the last ten years, DroneDeploy has built one of the most hardened photogrammetry pipelines in the world. It turns drone, ground, and 360° captures from over 3 million jobsites into maps, models, and measurements that construction teams rely on every day. We are now building its successor: a reconstruction stack that moves reality capture from hours to minutes and delivers substantially higher-quality 3D, built on learned methods like Gaussian splatting, feed-forward 3D foundation models (VGGT, MASt3R, MapAnything and their successors), learned matching and depth, and streaming reconstruction.

We are looking for a Senior or Staff engineer with deep specialist expertise in modern 3D reconstruction and spatial AI to design and build the core building blocks of that next-generation pipeline. You will be the team’s go-to expert in your area, shape the 3D reconstruction roadmap, and turn state-of-the-art research into reliable product features used on construction sites around the world.

This is a hands-on individual contributor role with real technical leadership and no people-management expectation. Your impact comes from what you build, the technical direction you set, and how you raise the bar for the engineers around you. Within your first 3–6 months, we expect a learned component you built to be running in production, at scale, on customer data.

What You’ll Do

• Build core reconstruction building blocks. Design, train, and ship learned 3D components that form the foundation of our pipeline: pose and geometry estimation, dense reconstruction, scene representation, and rendering.
• Bring next-generation 3D methods to production. Lead evaluation and integration of splatting, neural rendering, and 3D foundation models. Decide where they replace, augment, or initialize classical SfM and MVS.
• Push toward real-time reality capture. Develop fast, incremental, and streaming reconstruction that shortens the time from capture to usable 3D across drone, ground, and 360° capture.
• Own model architecture and training. Design architectures, losses, and training pipelines. Curate datasets from our capture corpus. Fine-tune and adapt foundation models to our domain.
• Shape the technical roadmap. Partner with ML leadership to identify which research bets to make, size their product impact, and turn them into concrete milestones.
• Set the bar for evaluation. Define benchmarks, metrics, and evaluation datasets for geometric accuracy, visual quality, robustness, and runtime. New methods win on evidence, not demos.
• Take research to production. Work with the engineers who own pipeline infrastructure to deploy models at scale on GPU cloud infrastructure, with attention to cost, latency, and reliability.
• Lead technically by example. Write design docs, give thoughtful code and architecture reviews, run reading groups, and help engineers level up in 3D ML.
• Stay at the frontier. Track CVPR, ICCV, ECCV, SIGGRAPH, and NeurIPS, prototype promising work quickly, and share what you learn.
• Use AI tooling every day. AI coding agents (e.g., Cursor, Claude Code) are a core part of how we work. Help define how the team uses them for research and engineering, and hold agent-written code to the same standard as human-written code.

What You Bring

• Deep, demonstrated expertise in at least one of the following, with working knowledge of the others:
  • Gaussian splatting, NeRF-family methods, or differentiable and neural rendering
  • Feed-forward 3D foundation models, learned SfM, or pose and depth regression
  • Streaming or real-time learned reconstruction (e.g., CUT3R, MASt3R-SLAM, splatting-based SLAM), beyond classical SLAM
  • Learned feature matching, learned multi-view stereo, or monocular depth estimation
• Strong grounding in multi-view geometry: camera models, bundle adjustment, SfM, and MVS. You know when classical methods beat learned ones, and when to combine them.
• Hands-on experience designing, training, and debugging 3D deep learning models in PyTorch.
• A track record of taking research ideas into working systems: production features, widely used open-source code, or strong published results.
• 5+ years of industry or applied research experience in 3D computer vision or 3D ML (a PhD in a related area counts). Master’s or PhD in Computer Science, Electrical Engineering, Robotics, or a related field, or equivalent practical experience.

Staff level additionally brings 8+ years, a record of setting technical direction across a team or organization, and ownership of multi-quarter technical bets from research question to shipped product.

Nice to Have

• Large-scale outdoor, aerial, or geospatial reconstruction, including georeferencing and accuracy against ground truth.
• 360° cameras, including spherical and equirectangular camera models.
• Modifying, fine-tuning, or retraining large 3D foundation models for new domains, data, or camera types.
• Mesh extraction from splats or radiance fields, surface reconstruction, or texturing.
• Large-scale multi-GPU training infrastructure.
• C++ or CUDA for performance-critical code.
• Publications at top venues (CVPR, ICCV, ECCV, SIGGRAPH, NeurIPS, 3DV) or notable open-source contributions (e.g., gsplat, COLMAP).
• Real-time or on-device 3D (ARKit, ARCore, mobile GPUs).
• Shipping GPU workloads on cloud platforms (AWS, GCP, Azure) with Docker and Kubernetes.

Additional Information

Base Pay Range:

140,960.00 - 193,820.00 USD Annual

This role may also be eligible for Equity Compensation and/or Bonus Incentive Compensation. Procore is committed to offering competitive, fair, and commensurate compensation. Actual compensation will be based on a candidate’s job-related skills, experience, education or training, and location.

For Los Angeles County (unincorporated) Candidates:

Procore will consider for employment all qualified applicants, including those with arrest or conviction records, in accordance with the requirements of applicable federal, state, and local laws, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act.

A criminal history may have a direct, adverse, and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment: 1. appropriately managing, accessing, and handling confidential information including proprietary and trade secret information, as well as accessing Procore's information technology systems and platforms; 2. interacting with and occasionally having unsupervised contact with internal/external customers, stakeholders, and/or colleagues; and 3. exercising sound judgment.