Senior Computer Vision Scientist – Basketball Tracking

SkillCorner SAS; Paris; On-site; EUR 90,000 - 140,000; Full time

Job summary

SkillCorner SAS is seeking a Senior Computer Vision Scientist to join our Basketball Tracking team in Paris. You will bring deep learning expertise to detection, tracking, re-identification and pose estimation, building production-grade models from research to deployment.

You will own end-to-end components of the tracking pipeline, design architectures, train and evaluate pipelines, and help raise engineering and research standards across the team, interfacing with clients globally.

Qualifications

• Graduate degree and 4+ years of hands-on CV with deep learning in production environments.
• Experience in detection, tracking, re-identification, pose estimation and related tasks.
• Strong Python for ML with clean, tested code and deployable packages (PyTorch or TensorFlow, OpenCV).
• Experience shipping models to production and working with data-centric ML workflows.

Responsibilities

• Design, train and optimize state-of-the-art DL models for detection, tracking and pose estimation.
• Own end-to-end components of the basketball tracking pipeline from data to deployment.
• Make architectural decisions and defend trade-offs with solid theoretical grounding.
• Monitor state-of-the-art models and translate ideas into production improvements.
• Establish metrics and tooling to measure tracking quality and drive continuous improvement.
• Collaborate with a global client base and mentor junior teammates.

Skills

Deep learning

Computer vision

Python for ML

PyTorch

TensorFlow

OpenCV

AWS

Docker

Production systems

English communication

Education

Master’s or PhD in engineering/CS/mathematics

Tools

PyTorch

TensorFlow

OpenCV

AWS

Docker

Job description

At SkillCorner, we turn a single broadcast camera feed into rich sports data, 100% automatically. Our Computer Vision and Machine Learning algorithms detect every player, the ball and the referees, locate them on the field, track them frame by frame and recognize who is who.

Since 2016, we have grown into a global leader in our field. We cover more than 150 men’s and women’s competitions, hold over 100 billion data points on more than 100,000 professional players, and are trusted by more than 350 football, basketball and American football teams. Clubs, federations and agencies use our data to rethink how they analyze performance, scout and recruit.

We are a team of about 150 people around the world, with our tech team based mostly in Paris. The Computer Vision team, 16 people strong, sits at the heart of our mission: it uses state‑of‑the‑art deep learning to deliver precise, high-quality tracking data, at scale and in production.

The role

We are looking for a Senior Computer Vision Scientist to join our Basketball Tracking team, reporting to the Team Lead. You will bring deep expertise in deep learning to the models that power our basketball tracking data, from detection and tracking to re-identification, recognition and pose estimation.

You will own key models end to end: designing architectures, building training and evaluation pipelines, and pushing performance to production‑grade quality. You will make architectural decisions with real downstream impact and help raise the research and engineering standards of the team.

This role sits where applied research meets production. Your work will go from experiment to live deployment, used by the professional basketball teams SkillCorner works closely with around the world.

What you’ll do

• Design, train and optimize state‑of‑the‑art deep learning models (CNNs, Transformers, GNNs) for detection, tracking, re‑identification, recognition and pose estimation

• Own key components of the basketball tracking pipeline end to end, from data and training to evaluation and deployment

• Make and defend architectural decisions, grounded in a solid understanding of the theory behind the models you build

• Monitor the state of the art, analyze model performance and turn new ideas into concrete improvements

• Establish the metrics and tooling that measure tracking quality, and drive continuous improvement from one release to the next

Engineering excellence

• Bring research algorithms into our AWS production pipeline and keep performance stable when new models go live

• Optimize models for accuracy, speed and cost at scale

• Raise the bar on engineering and research practices: code reviews, testing, reproducible experiments, documentation and smart use of LLM‑based tools

• Investigate issues in the pipeline and keep code quality high

• Understand client needs and product stakes, and translate them into concrete tracking improvements

• Share your knowledge and support more junior team members as they grow

• A graduate degree or PhD in engineering, computer science, mathematics or a related field

• 4+ years of hands‑on experience in computer vision with deep learning, including production systems

• Strong deep learning expertise in areas such as people tracking, re‑identification, people, face and object recognition, camera calibration, body pose estimation and action recognition

• Proven experience designing, training and shipping detection, tracking or recognition models to production, not only research prototypes

• Experience with data‑centric work: dataset curation, annotation strategies and handling noisy labels

• A strong grasp of the theory behind modern architectures (CNNs, Transformers, GNNs), and the judgment to choose between approaches

• Experience assembling several algorithms into one reliable system

• The ability to drive technical decisions independently and explain trade‑offs

• Strong Python for ML: clean, tested, deployable code, with PyTorch or TensorFlow and OpenCV

• Hands‑on experience with AWS workflows and Docker

• Excellent communication skills in English

Nice to have

• French: a strong plus, as the team is French‑speaking

• Experience with multi‑object tracking and re‑identification in crowded scenes, or with sports or broadcast video

• Experience with camera calibration, homography estimation or 3D geometry

• Experience running ML pipelines at scale in the cloud (GPU inference, batch processing)

• Notions of MLOps and model optimization

• Experience using LLM‑based agents to speed up development, debugging or support

• Publications at top computer vision or ML venues (CVPR, ICCV, ECCV, NeurIPS)

• Knowledge of, or a genuine interest in, basketball

Why join us

• Real impact: your work ships to production and is used by more than 350 professional teams worldwide

• Hard, exciting problems: state‑of‑the‑art computer vision on real broadcast video, at scale

• Ownership: own key models end to end and shape how our tracking technology evolves

• A great team: passionate people at the crossroads of AI and sport, with a tech team in the heart of Paris

We keep the process simple and transparent:

• 1- Phone interview (20–30 min)

• 2- Technical interview (1 h)

• 3- Technical test

• 4- Culture fit interview (1 h)