Senior Machine Learning Engineer - Esports
Role details
Job location
Tech stack
Job description
- 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 could be ideal for you!
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., * 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, * Flexible working conditions
- Mental health support
- Company and team-wide events
- Work travel opportunities with travel insurance
- Training and development budget
Requirements
Do you have experience in Python?, Do you have a Bachelor's degree?, * 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, * 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
Benefits & conditions
Pulled from the full job description
- Opportunities for advancement