CaliAlfa powers next-generation sports intelligence with ML models trained on deep historical data to forecast in-game behavior and results.

We are seeking a Lead Machine Learning Engineer to own the design and productionization of large-scale ML systems powering our sports betting platform. This role combines machine learning, distributed systems, and data engineering, with a strong focus on cost-efficient architectures operating on massive, delta-based datasets.

 Key Responsibilities

• Define and lead CaliAlfa’s ML strategy and standards: Set how ML is built, trained, deployed, and scaled across teams, acting as technical lead/mentor and raising engineering quality.

• Own our real-time sports pricing flow: Drive the architecture, monitoring and execution of the core system that prices live sports events with ultra-low latency and high reliability.

• Own the ML platform and infrastructure: Build and evolve core MLOps tooling and foundations—IaC, CI/CD, monitoring, testing, and operational support.

Required Experience

• Strong engineering fundamentals: Proven experience with SOLID principles, design patterns, and strongly typed languages (Go, Rust, Kotlin, Java, C#).

• Production ML expertise: Strong Python experience building and operating production-grade ML systems end-to-end.

• Distributed / event-driven systems: Solid background in distributed systems, parallel execution, and event-driven architectures.

• Cloud + data stack: Hands-on experience with AWS data services, SageMaker, and Iceberg.

• Performance optimization: Familiarity with Python acceleration tooling such as CUDA and Numba.