ML Engineer

• Stand up, monitor, maintain, and improve existing ML pipelines, ensuring they run reliably and efficiently in production on AWS.

• Engineer, evaluate, and iterate on model features — maintain a catalogue of engineered features for understanding what drives predictive performance and why it matters for live audience use cases.

• Develop new ML models and pipelines that expand our platform's capabilities, with a focus on practical, client-facing outcomes.

• Design scalable, IaC-managed ML pipelines and CI/CD systems that reliably deliver model training and inference deployment across hundreds of tenants.

• Work within AWS SageMaker as your primary ML environment — managing experiments, training jobs, model deployment, and monitoring.

• Collaborate closely with data engineers, developers, and product stakeholders to translate business questions into well-defined ML problems.

• Write clean, well-documented Python code that others on the team can build on.

• Invest in continuous learning — staying current on developments in ML, cloud infrastructure, and the evolving sports data landscape.

What you must have:

• A Computer Science degree or equivalent hands-on experience — we care about what you can do, not just where you studied.

• Solid SQL skills and basic data engineering fundamentals — querying, aggregating, and transforming structured and semi-structured data to support ML workflows.

• Proficiency in Python, with comfort writing modular, maintainable code using version control (GitHub or similar) as part of a collaborative team.

• Experience with cloud platforms (AWS, GCP, or Azure) and managed ML services (SageMaker, Vertex AI, AzureML); Sagemaker preferred

• Working knowledge of the ML lifecycle end-to-end: data prep, feature engineering, model training, evaluation, and production deployment.

• Familiarity with widely used ML algorithms (random forests, gradient boosting, regression), understanding when to use them and core concepts like feature importance and model evaluation metrics.

• Bonus: exposure to other AWS services (S3, Lambda, Glue, Redshift), Docker, or experience in a sports, media, or entertainment data context.

Salary/Rate Range: $80,000.00 - $95,000.00


Thank you for your interest in this opportunity. If you are selected to move forward in the process, we will contact you directly. If you do not hear from us, we encourage you to continue visiting our website for other roles that may be a good fit.