Haystack

ml engineer in sports technology

In short

This remote senior/lead ML Engineer role is for a sports technology company, sourced via recruiting agency Haystack. The job covers leading AI/ML solution development (Computer Vision, Generative AI, data science) for sports metadata, event detection, and player analytics, plus owning MLOps and production AI systems end-to-end. It requires extensive lead-level ML engineering experience, advanced Python with PyTorch/TensorFlow, full MLOps lifecycle expertise, and experience mentoring other data scientists.

About
Haystack is hiring on behalf of a partner that develops sports technology focused on innovation.
Responsibilities
•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 into player performance, contextual statistics, and injury risk by designing models with responsible and ethical AI principles embedded 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 data use
•Define advanced experimental designs, lead A/B testing, and develop and maintain metrics and dashboards
•Establish robust MLOps practices and own 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, ensuring resilient performance during peak traffic, responsible real-time model behavior, and an optimal balance between cost, latency, and production-scale performance
Requirements
•Extensive lead-level engineering experience delivering data-driven ML systems, with ownership of technical direction, mentoring, and delivery
•Working knowledge of modern ML techniques, including Generative AI, and using emerging models to extract insights from multimodal sports data such as numerical, spatial, video, or metadata
•Advanced Python expertise and hands-on experience with ML/DL frameworks such as PyTorch and TensorFlow, including taking models from experimentation into production 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, including 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, with adaptability and the ability to support the team through uncertainty and pivot as necessary
Conditions
•Competitive salary
•Access to advanced technologies and tools for AI development
•Professional growth and development opportunities in a dynamic tech environment
•Collaborative work environment focused on innovation and advancing sports technology

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