Asks for

The ad says“MLOps foundations: feature stores, experiment tracking, model registries (MLflow/W&B-class), continuous training pipelines.”
The ad says“Experience owning models in production: deployment, monitoring, drift detection, retraining — with real latency budgets, not just research notebooks.”
The ad says“MLOps foundations: feature stores, experiment tracking, model registries (MLflow/W&B-class), continuous training pipelines.”
The ad says“MLOps foundations: feature stores, experiment tracking, model registries (MLflow/W&B-class), continuous training pipelines.”
The ad says“Model serving and inference optimization (vLLM-class serving, quantization).”
The ad says“Basic LLM knowledge, especially how to use it and where not to use it.”
The ad says“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.”
The ad says“MLOps foundations: feature stores, experiment tracking, model registries (MLflow/W&B-class), continuous training pipelines.”
The ad says“Experience owning models in production: deployment, monitoring, drift detection, retraining — with real latency budgets, not just research notebooks.”
The ad says“Experience owning models in production: deployment, monitoring, drift detection, retraining — with real latency budgets, not just research notebooks.”
The ad says“MLOps foundations: feature stores, experiment tracking, model registries (MLflow/W&B-class), continuous training pipelines.”
The ad says“Model serving and inference optimization (vLLM-class serving, quantization).”
The ad says“Supervised learning, transfer leaning on machine learning as well as neural networks”
The ad says“Model serving and inference optimization (vLLM-class serving, quantization).”
The ad says“Experience owning models in production: deployment, monitoring, drift detection, retraining — with real latency budgets, not just research notebooks.”
The ad says“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.”
The ad says“Supervised learning, transfer leaning on machine learning as well as neural networks”

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:

Modeling Depth

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).

Technology Familiarity

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).

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.

Benefits:

We are passionate about fostering a supportive environment for our team, so we prioritize the physical, mental, and emotional well-being of our employees and their families through a comprehensive Benefits Program. This includes 100% company-paid medical, dental, and vision plans, unlimited Flexible Time Off, and a personalized career roadmap for each US employee. By investing in professional development through training and educational opportunities, we ensure that our team thrives both personally and professionally. Together, we’re not just building a business; we’re cultivating a community that values creativity, collaboration, and the transformative power of play.

Equal Employment Opportunity Statement:

Xsolla is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based on race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity, or any other characteristic protected by law.

We consider qualified applicants with criminal histories in accordance with the Fair Chance Act.

Criminal History Consideration:

For the Data Scientist position, we will conduct a background check that may include the following:

Criminal history check

Employment verification

Education verification

Relevance to Job Responsibilities:

The background check is relevant to this position because of the following role responsibilities:

Accessing confidential company data

Ensuring compliance with regulatory requirements

Rights Under the Fair Chance Act:

Applicants are encouraged to inquire about their rights under the Fair Chance Act. If you have questions regarding our hiring practices, please contact careers@xsolla.com.

Asks for

Values

Requirements:

Nice-to-Have: