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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** Tripledot Studios - **Location:** UK (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Business Logic, Artificial Neural Networks, Relational Databases, Software Debugging, Machine Learning, Query Optimization, Recommender Systems, Tensorflow, Azure Machine Learning, SQL Databases, Pytorch, Delivery Pipeline, Large Language Models, Salesforce Lightning, Scikit Learn, Xgboost, Machine Learning Operations, Marketplace - **Published:** September 25, 2026 - **Apply:** https://startup.jobs/senior-machine-learning-engineer-tripledot-studios-company-10176996 ## About the Role * Hands-on training and evaluation of tabular models, using neural networks or gradient-boosted trees. Experience with PyTorch, PyTorch Lightning, TensorFlow, XGBoost, CatBoost or scikit-learn could all be relevant; no single framework is required. * Experience designing features and labels, choosing metrics for the decision a model makes and judging when offline results warrant an A/B test. * Proficiency in SQL and the ability to write efficient queries to extract, manipulate and aggregate data from relational databases. * An investigative approach to incomplete data and longer-term requirements that need clarification. * The ability to explain model results to monetization and product partners in terms of revenue and player outcomes. * Experience in ad tech, recommender systems or online marketplaces would be useful, as would experience with production APIs or deploying ML models. Experience with the Ray framework would also be a plus. None of these is required for the role. * Uses AI-assisted development tools, including code assistants and LLM-based copilots, to accelerate implementation, debugging and iteration of machine learning systems while maintaining production-quality standards. * Critically reviews and validates AI-generated code, model implementations and infrastructure configurations for reliability, correctness and maintainability, and explores AI-powered approaches to improve developer productivity or ML platform capability. ## Description Join Tripledot Studios as a Senior Machine Learning Engineer working on dynamic pricing and recommender systems use cases in our games. The work involves tabular machine learning, including neural networks and approaches such as gradient-boosted decision trees. You'll focus on understanding the data, developing features and improving the models, rather than primarily owning production deployment. You'll work toward defined near-term ML targetds, collaborating with the monetization team and game product teams to plan A/B tests. Over the longer term, you'll work with those teams to translate product and monetization requirements into ML objectives. You'll start by learning the project and its business goals. Within the first three months, we'd expect you to contribute to the training pipeline and investigate features and model issues. By around six months, the aim is for you to take a more proactive role in setting the model's direction., * Build and improve training pipelines for dynamic pricing and recommender system models, from feature and label design through training, tuning and offline evaluation of tabular models such as neural networks and gradient-boosted trees. * Monitor model performance once models are live across games and products. Investigate data and concept drift, including shifts tied to new titles, client versions or user behaviour, and make models easier to extend to another product. * Diagnose missed model outcomes across the training pipeline, business logic and underlying data, then work through the improvements needed to restore model quality. * Work with monetization and product colleagues to connect model decisions to revenue and player outcomes, and help plan the A/B tests that inform what ships. * As you get up to speed, identify gaps in the team's understanding of the models and propose improvements to their direction. ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [TikTok's Privacy Innovation](https://www.wearedevelopers.com/videos/1036-tiktok-s-privacy-innovation) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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