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Tripledot Studios, one of the world's largest independent mobile games companies with 2,500+ employees across 12 studios, is seeking a Senior Machine Learning Engineer to work on dynamic pricing and recommender systems for their games portfolio (25M+ daily active users).
You will focus on tabular machine learning—neural networks and gradient-boosted decision trees—with emphasis on understanding data, feature engineering, and model improvement rather than production deployment ownership. You'll collaborate with monetization and game product teams to plan and execute A/B tests, translating product and monetization requirements into ML objectives.
Ramp-up timeline: Within three months, contribute to training pipelines and investigate features and model issues. By six months, take a proactive role in setting model direction.
Key Responsibilities:
- Build and improve training pipelines for dynamic pricing and recommender models, covering feature and label design through training, tuning, and offline evaluation of tabular models (neural networks, gradient-boosted trees).
- Monitor live model performance across games and products; investigate data and concept drift tied to new titles, client versions, or user behavior; extend models to new products.
- Diagnose missed outcomes across training pipeline, business logic, and underlying data; implement improvements to restore model quality.
- Partner with monetization and product teams to connect model decisions to revenue and player outcomes; help plan A/B tests that inform shipping decisions.
- Identify gaps in team understanding of models and propose directional improvements as you ramp up.
Requirements:
- Hands-on experience training and evaluating tabular models using neural networks or gradient-boosted trees (PyTorch, PyTorch Lightning, TensorFlow, XGBoost, CatBoost, or scikit-learn).
- Experience designing features and labels, choosing metrics for model decisions, and judging when offline results warrant A/B testing.
- Proficiency in SQL and ability to write efficient queries to extract, manipulate, and aggregate data from relational databases.
- Investigative approach to incomplete data and requirements needing clarification.
- Ability to explain model results to monetization and product partners in terms of revenue and player outcomes.
- Proficiency with AI-assisted development tools (code assistants, LLM-based copilots) to accelerate ML system implementation, debugging, and iteration while maintaining production quality.
- Ability to critically review and validate AI-generated code, model implementations, and infrastructure configurations for reliability, correctness, and maintainability.
- Nice-to-have: experience in ad tech, recommender systems, online marketplaces, production APIs, ML model deployment, or Ray framework.
