ml engineer in sports technology

Haystack’s partner develops sports technology solutions using Computer Vision, Machine Learning, Generative AI, and data science to generate sports metadata, detect key events in live content and data streams, and deliver player performance insights and personalized recommendations.

• Lead the end-to-end development of AI solutions using Computer Vision, Machine Learning, Generative AI, and data science for automated sports metadata generation and key-event detection in live content and data streams;
• Generate actionable insights for player performance, contextual statistics, and injury risk by designing models with responsible and ethical AI principles from design through deployment;
• Integrate model-driven insights into personalization engines for recommendations based on favorite teams, players, match context, and other signals while ensuring transparency, fairness, and appropriate data use;
• Define advanced experimental designs and lead A/B testing;
• Develop and maintain metrics and dashboards;
• Establish robust MLOps practices;
• Own end-to-end productionization from data ingestion through deployment and ongoing model monitoring;
• Design, architect, and operate low-latency, highly reliable cloud-based AI systems for live sports scenarios;
• Ensure resilient performance during peak traffic, responsible real-time model behavior, and an optimal balance between cost, latency, and production-scale performance.

• Extensive lead-level engineering experience delivering data-driven ML systems with clear ownership of technical direction, mentoring, and delivery;
• Working knowledge of modern ML techniques, including Generative AI and extracting insights from multimodal sports data such as numerical, spatial, video, and metadata;
• Advanced Python expertise and strong hands-on experience with ML/DL frameworks such as PyTorch and TensorFlow;
• Experience taking models from experimentation into production model serving;
• End-to-end MLOps experience, including CI/CD for ML, experiment tracking, model registries, drift detection, automated retraining, and infrastructure as code;
• Technical leadership experience mentoring and guiding Senior and Mid-Level Data Scientists in day-to-day work and career development;
• Adaptability and ability to support a team through uncertainty and pivot as necessary.

• Competitive salary;
• Access to advanced technologies and tools for AI development;
• Opportunities for professional growth and development in a dynamic tech environment;
• Collaborative work environment focused on innovation in sports technology.