ml engineer for sports technology

Haystack is a technology and information partner operating in sports technology, using AI to generate sports metadata, detect key events in live content and data streams, and support 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 detection of key events in live content and data streams;
• Generate actionable insights for player performance, contextual statistics, and injury risk by designing models with embedded responsible and ethical AI principles from design through deployment;
• Integrate model-driven insights into personalization engines and tailor recommendations based on favorite teams, players, match context, and other signals while ensuring transparency, fairness, and appropriate use of data;
• 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 model behavior in real time, and an optimal balance between cost, latency, and production scale performance.

• Proven 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, or metadata;
• Advanced Python expertise with strong hands-on use of ML/DL frameworks such as PyTorch and TensorFlow, including 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 practices;
• Proven technical leadership experience mentoring and guiding Senior and Mid-Level Data Scientists in their day-to-day work and career development;
• Experience working in a fast-changing environment, demonstrating adaptability and supporting the team through uncertainty and necessary pivots.

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