Research Scientist — 3D/4D Reconstruction
- Zurich, Switzerland
- Full-time
- As soon as possible
About Athlence
Athlence is building technology to capture live sports events in 4D and render them from novel viewpoints to create immersive, broadcast-quality experiences. Our technology combines synchronized multi-camera video, 3D reconstruction, and generative models to build highly realistic representations of dynamic sports scenes.
We are a Zurich-based deep-tech startup working at the intersection of computer vision, machine learning, and high-performance engineering. We develop and test our technology in real-world professional sports environments, where reconstruction quality, robustness, and system performance all matter.
About the role
We are looking for a Research Scientist to advance the 3D reconstruction technology behind Athlence’s capture and rendering pipeline.
The 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.
The 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.
What you will be doing
- Research and develop 3D/4D Gaussian Splatting and modern feed-forward 3D models for high-quality dynamic scene reconstruction and novel-view synthesis
- Develop methods for reconstructing dynamic scenes from sparse multi-view observations while preserving geometry, appearance, and temporal consistency
- Explore model architectures, scene representations, training objectives, and other approaches to improve reconstruction quality, robustness, latency, and compute efficiency
- Design and run rigorous experiments to understand model behavior, failure modes, and trade-offs between visual fidelity, geometric consistency, runtime, and compute
- Develop evaluation metrics and benchmarks that capture reconstruction quality across geometry, appearance, and novel-view rendering
- Build reliable training, evaluation, and inference pipelines for 3D reconstruction models.
- Depending on your interests, contribute to the data pipeline, including processing, filtering, and curating large-scale multi-camera datasets for training and evaluation.
- Collaborate closely with generative modeling and hardware/systems engineers to connect reconstruction methods with capture infrastructure and the broader rendering pipeline
What you must have
- Strong hands-on experience in deep learning and computer vision, demonstrated through research, industry experience, or a combination of both
- Strong foundations in multi-view geometry, 3D reconstruction, and camera calibration, including the practical challenges of real-world capture systems
- Deep expertise in at least one of the following areas: 3D/4D Gaussian Splatting, feed-forward 3D reconstruction, neural rendering, or novel-view synthesis
- Strong Python and PyTorch skills, with experience building reliable training and evaluation pipelines
- Experience designing experiments, analyzing model behavior, and investigating failure modes
- Ability to take open-ended research ideas from initial hypotheses through implementation, experimentation, and evaluation.
- Ability to work with a high degree of autonomy in a fast-paced, highly technical, and collaborative environment.
- Strong technical communication skills, with the ability to explain findings, assumptions, and tradeoffs clearly.
Nice to have
- Experience with dynamic scene reconstruction, temporal 3D modeling, or 4D scene representations
- Experience training deep learning models across multi-GPU or multi-node environments, including distributed PyTorch training
- Experience optimizing 3D or deep learning models for training and inference, including memory, throughput, and runtime performance
- Publications 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
- Experience shipping research-driven computer vision systems into real-world or production environments
- Experience with modern generative image or video models such as diffusion or flow-based models
What we offer
- Access to unique, proprietary multi-camera sports data captured in real-world environments
- The opportunity to work on challenging 3D vision problems where research ideas are tested against real production constraints
- High ownership across the full research cycle, from research exploration and experimentation to integration and deployment
- A small, highly technical team where you can directly shape both the research direction and the systems we build
How to apply
Please send your CV and a one-page note about the most interesting 3D reconstruction or novel-view synthesis system you have built or worked on. Tell us about the problem you were solving, the approach you chose, the data and evaluation setup, what worked, and where the system failed. A GitHub profile, project page, publication, demo, or other example of your work is highly encouraged.
- Send to
- hiring@athlencesports.com
- Subject line
3D Reconstruction — [Your Name]
Also open