Senior Machine Learning Engineer
About ReSpo.Vision
ReSpo.Vision is an AI and Computer Vision company transforming how sports are analyzed, visualized, and monetized. Our proprietary single-camera system extracts elite-level tracking data and performance analytics from standard broadcast or tactical video, without wearables or in-venue installations. Already used by global clubs, federations, and competitions like FIFA, Concacaf, or CONMEBOL, we are actively expanding into media, fan engagement, and betting applications.
Our pipeline combines advanced computer vision and deep learning models to track all players and the ball in 3D using a single-camera feed. The resulting positional data powers our growing product suite: from match analytics platform to visual content, including 3D match reconstructions (see an early demo: YouTube) and real-time broadcast augmentation with dynamic stats and virtual overlays. The system is built for scalability, leveraging cloud-native infrastructure, GPU inference pipelines, and sports-specific post-processing modules that turn raw detections into meaningful insights.
We’re now entering an exciting phase, pushing our tracking system toward real-time applications, lowering latency to enable live insights and instant visual augmentations. In parallel, we’re building new layers on top of our core data, including realistic 3D match reconstructions and virtual overlays for enhanced broadcast experiences.
As a Machine Learning Engineer at ReSpo.Vision, you’ll play a key role in shaping these products, enabling real-time, visually engaging experiences across sports and media.
Your Role
You will be responsible for:
- Designing, training, testing, and validating machine learning models for classification, regression, object detection, and segmentation
- Choosing appropriate architectures and optimization techniques
- Performing data quality analysis, feature engineering, and experimentation
- Preparing ML models for deployment in production environments
- Developing robust MLOps pipelines for training, testing, and monitoring
- Writing clear documentation and conducting peer code reviews
- Participating in ML system architecture planning and data strategy development
Who You Are
- A proactive ML Engineer with a strong academic background and commercial experience
- Skilled in PyTorch with 3–4 years of practical application
- Comfortable working with Linux and cloud environments (AWS or GCP)
- Experienced in working with image and video data, including detection and segmentation tasks
- Capable of owning end-to-end ML projects, from data to deployment
- Fluent in English (min. B2 level)
- Analytical, autonomous, and comfortable with uncertainty and iteration
Nice to have
- Knowledge of Hugging Face libraries ecosystem, YOLO and Detectron
- Experience with scalable ML systems and low-latency inference
- Interest in sport analytics and complex data environments
What we offer
- A chance to work with a top-tier engineering team, including Kaggle Grandmasters
- Hybrid work model
- Flexibility in employment type (B2B/contract of employment)
- Market-level salary and 80-85% authorship cost deduction
- Private healthcare, Multisport card
- Open training and development budget aligned with your career goals
- Ownership and autonomy – no micromanagement, real impact
- A unique opportunity to shape a globally recognized, high-impact product used by top sports organizations like Chelsea, Paris Saint-Germain, or FIFA
Please include the following clause in your CV: I hereby give consent for my personal data to be processed by ReSpo.Vision sp. z o.o. for the purposes of this recruitment process.
Read our privacy polity at: https://respo.vision/privacy