Principal / Staff Data Scientist
Confirmed on the employer's own hiring board on Oct 25, 2026. First seen by Alion on Jul 29, 2026. Xsolla scores B on the Alion truth index.
ABOUT YOU
We are looking for an accomplished Principal Machine Learning Engineer to join our global ML organization. In this role, you will drive innovation across our machine learning ecosystem, architect advanced ML solutions, and mentor junior ML engineers around the world. You will play a key part in shaping our technical direction-leading complex ML initiatives, elevating engineering standards, and guiding teams as they build scalable, production-ready machine learning systems.
If you are ambitious, energized by solving challenging technical problems, passionate about developing talent, and excited to influence the future of ML/AI in the video game industry, this could be the perfect role for you.
ABOUT US
Xsolla is a global commerce company with robust tools and services to help developers solve the inherent challenges of the video game industry. From indie to AAA, companies partner with Xsolla to help them fund, distribute, market, and monetize their games. Grounded in the belief in the future of video games, Xsolla is resolute in the mission to bring opportunities together, and continually make new resources available to creators. Headquartered and incorporated in Los Angeles, California, Xsolla operates as the merchant of record and has helped over 1,500+ game developers to reach more players and grow their businesses around the world. With more paths to profits and ways to win, developers have all the things needed to enjoy the game.
For more information, visit xsolla.com.
Requirements:
Advanced Degree in Statistics, machine learning or related areas. Experience in statistics/ML expertise with a track record of leading high-impact data science initiatives at scale of billions of transactions.
Hands-on experience creating, training and fine-tuning models not just integrating hosted model APIs. You should be able to walk through the data, the objective, what broke, and the before/after evaluation numbers, and why the model did not perform as expected.
Experience owning models in production: deployment, monitoring, drift detection, retraining - with real latency budgets, not just research notebooks.
Production experience with classical ML for fraud/anomaly detection, recommendation, or churn/LTV (gradient boosting, deep learning, graph-based models).
Supervised learning, transfer leaning on machine learning as well as neural networks
Basic LLM knowledge, especially how to use it and where not to use it.
MLOps foundations: feature stores, experiment tracking, model registries (MLflow/W&B-class), continuous training pipelines.
Model serving and inference optimization (vLLM-class serving, quantization).
Modeling Depth
Technology Familiarity
Nice-to-Have:
Publications, conference talks, or recognized open-source contributions to training/eval tooling .
Graph-based fraud detection (fraud rings, device/account linkage)..
Gaming, payments, fraud, advertising domain experience.
Hands-on, up-to-date experience with modern AI tools (e.g., Claude, Copilot, Cursor) for code generation, review, and accelerating day-to-day engineering work.