This is an applied research role with a strong ownership mandate – youll drive the full research lifecycle from problem framing and experimental design through to production integration.
We are dedicated to revolutionizing sports production. Our AI-powered solutions automatically capture sports events and transform them into personalized content, turning key moments into engaging experiences for players, teams, and fans. As a Senior AI Research Scientist, you will have direct impact on the companys innovation roadmap, leading core AI capabilities that power our platform at scale.
As part of your role, you will:
Own the research lifecycle end-to-end: problem definition, literature review, experimental design, model development, evaluation, and production integration.
Design, implement, and evaluate state-of-the-art deep learning models for computer vision tasks including detection, tracking, event recognition, and temporal modeling in video.
Drive applied research projects with clear product impact – not just experimentation, but shipping models that run reliably in production.
Collaborate closely with ML engineers, infrastructure engineers, and QA to ensure smooth model integration and performance in real-world conditions.
Contribute to our research culture: present findings internally, propose new research directions, and stay at the forefront of computer vision and deep learning.
Influence architectural decisions around model design, data pipelines, and evaluation frameworks.
Requirements:
M.Sc. or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Electrical Engineering, or an equivalent field (A Must).
5+ years of hands-on experience in applied computer vision research and development, with a clear track record of delivering results.
Deep expertise in deep learning – strong theoretical foundations and practical mastery of model training, evaluation, and debugging.
Strong proficiency in Python and PyTorch (or equivalent frameworks); comfort reading and writing production-quality code.
Proven experience working with real-world video data: preprocessing, annotation pipelines, training at scale, and production deployment.
Ability to work independently, set your own research agenda, and drive projects to completion with minimal guidance.
Strong communication skills – ability to explain research decisions to both technical peers and non-technical stakeholders.
Bonus points if you have:
Experience with video understanding tasks: action recognition, temporal modeling, multi-camera setups, or sports analytics.
Experience training Vision-Language Models (VLMs).
Familiarity with model optimization techniques for inference.
Publications in top-tier venues (CVPR, ICCV, ECCV, NeurIPS, ICLR, or equivalent).
