Xsolla
via Lever
Principal / Staff Data Scientist
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 2d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 47d ago
The date the source published, not the day we noticed it (2026-07-29). Last seen at its source 2h ago.
Is it remote?
Remote USA
That is the location the employer filed this posting under. Quoted as written — we do not re-word the source's own location.
Who may apply?
United States
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $175k–238.5k/yr
Middle 50% of 105 listings that do state pay — Operations · Lead · United States · USD/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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lever employer's own board first seen 38d ago · last seen 2h ago
The listing
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.
Requirements:
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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.
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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.
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Experience owning models in production: deployment, monitoring, drift detection, retraining — with real latency budgets, not just research notebooks.
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Production experience with classical ML for fraud/anomaly detection, recommendation, or churn/LTV (gradient boosting, deep learning, graph-based models).
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Supervised learning, transfer leaning on machine learning as well as neural networks
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Basic LLM knowledge, especially how to use it and where not to use it.
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MLOps foundations: feature stores, experiment tracking, model registries (MLflow/W&B-class), continuous training pipelines.
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Model serving and inference optimization (vLLM-class serving, quantization).
Modeling Depth
Technology Familiarity
Nice-to-Have:
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Publications, conference talks, or recognized open-source contributions to training/eval tooling .
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Graph-based fraud detection (fraud rings, device/account linkage)..
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Gaming, payments, fraud, advertising domain experience.
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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.