pWe are looking for a Research Scientist to advance the 3D reconstruction technology behind Athlence’s capture and rendering pipeline. /ppThe focus of this role is on reconstructing fast-moving dynamic scenes from sparse multi-view inputs and rendering them from novel viewpoints while maintaining high visual fidelity, temporal consistency, and computational efficiency. You will work with 3D/4D Gaussian Splatting, feed-forward 3D models, and other reconstruction approaches, adapting existing methods and developing new ones for robust, photorealistic reconstruction. /ppThe role sits at the intersection of 3D vision and high-performance systems. You will work closely with researchers in generative modeling and with hardware/systems engineers to connect reconstruction methods with real-world capture infrastructure and develop models that operate reliably on multi-camera sports data. /ph3What you will be doing /h3ulliResearch and develop 3D/4D Gaussian Splatting and modern feed-forward 3D models for high-quality dynamic scene reconstruction and novel-view synthesis /liliDevelop methods for reconstructing dynamic scenes from sparse multi-view observations while preserving geometry, appearance, and temporal consistency /liliExplore model architectures, scene representations, training objectives, and other approaches to improve reconstruction quality, robustness, latency, and compute efficiency /liliDesign and run rigorous experiments to understand model behavior, failure modes, and trade-offs between visual fidelity, geometric consistency, runtime, and compute /liliDevelop evaluation metrics and benchmarks that capture reconstruction quality across geometry, appearance, and novel-view rendering /liliBuild reliable training, evaluation, and inference pipelines for 3D reconstruction models. /liliDepending on your interests, contribute to the data pipeline, including processing, filtering, and curating large-scale multi-camera datasets for training and evaluation. /liliCollaborate closely with generative modeling and hardware/systems engineers to connect reconstruction methods with capture infrastructure and the broader rendering pipeline /li /ulh3What you must have /h3ulliStrong hands-on experience in deep learning and computer vision, demonstrated through research, industry experience, or a combination of both /liliStrong foundations in multi-view geometry, 3D reconstruction, and camera calibration, including the practical challenges of real-world capture systems /liliDeep expertise in at least one of the following areas: 3D/4D Gaussian Splatting, feed-forward 3D reconstruction, neural rendering, or novel-view synthesis /liliStrong Python and PyTorch skills, with experience building reliable training and evaluation pipelines /liliExperience designing experiments, analyzing model behavior, and investigating failure modes /liliAbility to take open-ended research ideas from initial hypotheses through implementation, experimentation, and evaluation. /liliAbility to work with a high degree of autonomy in a fast-paced, highly technical, and collaborative environment. /liliStrong technical communication skills, with the ability to explain findings, assumptions, and tradeoffs clearly. /li /ulh3Nice to have /h3ulliExperience with dynamic scene reconstruction, temporal 3D modeling, or 4D scene representations /liliExperience training deep learning models across multi-GPU or multi-node environments, including distributed PyTorch training /liliExperience optimizing 3D or deep learning models for training and inference, including memory, throughput, and runtime performance /liliPublications at leading computer vision, graphics, or machine learning venues such as CVPR, ICCV, ECCV, SIGGRAPH, NeurIPS, or ICML, particularly in 3D vision or closely related areas /liliExperience shipping research-driven computer vision systems into real-world or production environments /liliExperience with modern generative image or video models such as diffusion or flow-based models /li /ulh3What we offer /h3ulliAccess to unique, proprietary multi-camera sports data captured in real-world environments /liliThe opportunity to work on challenging 3D vision problems where research ideas are tested against real production constraints /liliHigh ownership across the full research cycle, from research exploration and experimentation to integration and deployment /liliA small, highly technical team where you can directly shape both the research direction and the systems we build /li /ul #J-18808-Ljbffr
Research Scientist — 3D/4D Reconstruction
