Senior Machine Learning R&D Engineer

Senior Machine Learning R&D Engineer

Matterport is leading the digital transformation of the built world. Our groundbreaking spatial computing platform turns buildings into data making every space more valuable and accessible. Millions of buildings in more than 170 countries have been transformed into immersive Matterport digital twins to improve every part of the building lifecycle from planning, construction, and operations to documentation, appraisal, and marketing.

About the Role
Matterport has captured one of the largest and most diverse repositories of real-world 3D spatial data in existence. We are building a team to turn that data into a foundational reconstruction model — a model that can reconstruct, complete, understand, and generate photorealistic 3D spaces from sparse, incomplete, or noisy captures. As a Senior Machine Learning R&D Engineer on this team, you will research and build the next generation of neural reconstruction and generative systems that power Matterport's digital twins. Rather than reconstructing only what the camera saw, our goal is to generatively complete spaces — filling occlusions, synthesizing unseen views, upgrading capture fidelity, and ultimately generating entire coherent 3D environments. You will work at the intersection of neural rendering, novel view synthesis, and generative modeling, training large-scale models on Matterport's proprietary data to solve problems that no existing dataset or model can address. This is a hands-on research-and-engineering role for someone who wants to push the state of the art in 3D generative AI and ship it into a product used across 170+ countries. You will collaborate closely with fellow researchers, platform engineers, and product teams to translate frontier ideas into capabilities our customers can feel.

Responsibilities:
Research, design, and train large-scale foundational models for 3D reconstruction and generative scene completion, leveraging Matterport's proprietary corpus of real-world spatial data. Advance the state of the art in neural rendering (e.g., Gaussian splatting, NeRFs, feed-forward/large reconstruction models) and novel view synthesis for real-world indoor and outdoor spaces. Develop generative approaches — including diffusion models and video/3D generation — to complete occluded or missing regions, synthesize unobserved viewpoints, and enhance the realism and completeness of digital twins. Design, implement, and optimize scalable training and inference pipelines for large 3D and multi-view datasets, including images, video, point clouds, and meshes. Translate research prototypes into production-ready systems, partnering with engineering teams to integrate models into Matterport's core capture and reconstruction platform. Define rigorous evaluation methodologies and benchmarks for reconstruction quality, view synthesis fidelity, and generative plausibility; drive iterative improvements against them. Stay current with advances across neural rendering, generative modeling, and 3D vision, and proactively identify opportunities to apply new techniques to Matterport's challenges. Collaborate with product managers, software engineers, and researchers to scope projects, assess technical feasibility, and deliver high-impact features. Contribute to the architectural design and strategic roadmap of Matterport's foundational reconstruction model and broader ML research initiatives. Contribute to Matterport's intellectual property through patents and publications and represent our work to the broader research community. Basic

Qualifications:
Bachelor's degree from an accredited, not-for-profit, in-person University or College A track record of commitment to prior employers. Experience in machine learning research and development, with a strong focus on computer vision, 3D data processing, neural rendering, or generative modeling. Proficiency in PyTorch and strong programming skills in Python. Solid understanding of machine learning fundamentals, including deep learning architectures (e.g., CNNs, Transformers, diffusion models), optimization techniques, and evaluation methodologies. Experience with data manipulation and analysis libraries (e.g., NumPy, Pandas). Demonstrated ability to conduct independent research, propose novel solutions, and implement them effectively. Excellent problem-solving skills and the ability to work with complex, real-world datasets. Strong verbal and written communication skills Preferred

Qualifications:
Ph.
D. or Master's Degree in Computer Science, Computer Vision, Machine Learning, Graphics, Robotics, or a related quantitative field. 5+ years of industry experience in applied ML research and development, particularly training and shipping large models to production. Demonstrated expertise in building or training foundation / large-scale models and/or depth in two or more of the following: Neural rendering & novel view synthesis — Gaussian splatting, NeRFs, large/feed-forward reconstruct