-
Are you excited about building ML systems that make predictions in real-time?
-
Are you driven by building things end-to-end, from research to live systems?
If the answers to the above questions are yes, then this role will be ideal for you!
About the role
We are building real-time prediction systems for competitive esports (CS2, Dota2, League of Legends). Our models power live betting markets, producing continuously updated win probabilities, handicap lines, over/under totals, and specialty markets during matches.
We are looking for a Senior ML Engineer to own the full lifecycle of our prediction models: from research notebooks to production-grade ML pipelines, deployed at scale in a real-time microservices architecture.
What you will do
-
Convert existing model training code into reproducible, automated pipelines (experiment tracking, model versioning, automated retraining), following ML best practices
-
Work on algorithms and probabilistic market-derivation logic that powers our live predictions
-
Define evaluation metrics, build backtesting frameworks, and monitor model performance in production
-
Serve models via a Python microservices stack
-
Work with the product team to define new betting markets and the statistical models that support them
Your skills will include
-
5+ years of professional experience in ML engineering or applied data science
-
Experience developing production-grade ML pipelines and are familiar with workflow orchestration, experiment tracking and CI/CD for ML
-
Knowledge of object-oriented programming, using vector operations for optimized performance, and a deep understanding of memory management
-
A strong grasp of probability and statistics
Nice to have
-
Experience with real-time / streaming ML, models that update or serve live predictions
-
Familiarity with betting / trading / quantitative finance, understanding of odds, overround, market-making, or any domain where calibrated probabilities matter
-
Experience building MLOps infrastructure
-
Knowledge of esports or sports analytics