ML Scientist

At Metica we let game studios focus on making great games while we take care of growing them. Were an AI-native growth partner for mobile games studios unifying user acquisition and in-game monetisation so games grow without studios having to build that expertise in-house.

Were built by founders and an early team on their third venture together (previously built and exited to King and Apple) backed by Play Ventures 13books Capital and Firstminute Capital. Were already working with some of the biggest names in mobile gaming and are entering our scaling phase which means real ownership for whoever joins us next.

Hiring manager: Puli Liyanagama CTO

Youll own ML systems end to end from hypothesis through experimentation training evaluation deployment monitoring and iteration. Youll write production-quality training and inference code own your releases and be accountable for the business metrics your models improve.

• Own ML systems end to end hypothesis experimentation training evaluation deployment monitoring and iteration.

• Write production-quality training and inference code own releases and be accountable for the business metrics your models improve.

• Design execute and interpret A/B and multivariate experiments power analysis guardrails exposure/assignment strategy and statistical significance.

• Apply contextual bandits causal inference uplift modelling off-policy evaluation and propensity scoring to optimise real-world decisions.

• Analyse large-scale datasets diagnose changes in model or product performance and turn findings into clear recommendations for engineering product and commercial teams.

• Expert Python and SQL with experience building production ML systems and data pipelines (PyTorch XGBoost Spark/PySpark Apache Iceberg Ray MLflow SageMaker Airflow or equivalent).

• Deep experimentation and statistical inference skills and a strong grasp of optimisation under uncertainty (bandits causal inference off-policy evaluation).

• Data-driven and self-driven comfortable with ambiguity and able to work across engineering product and customer-facing teams.

• Demonstrated ownership of production business metrics not just research models is a plus as is effective use of AI-assisted and agentic software engineering workflows.