It’s an exciting time to join us! We’re entering new markets, developing new technologies, and moving step by step towards our goal of exciting the world. As our business grows, the number of exciting people initiatives grows with it, and we’re looking for a new colleague to partner with our team to bring these to life.
As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.
This role blends hands‑on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross‑functional collaboration.
What you’ll you be doing:
- Identify high‑impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
- Design and develop scalable machine learning models — including classifiers, regressors, and rule‑based systems — to solve real‑world problems.
- Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
- Translate complex technical concepts into clear insights for both technical and non‑technical stakeholders.
- Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
- Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.
We're looking for someone with:
- Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
- 7+ years of industry experience building and deploying ML models at scale.
- Proven ability to lead cross‑functional technical initiatives and influence engineering strategy.
- Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit‑learn) and SQL.
- Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
- Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
- A track record of shipping production‑level ML products and maintaining high code quality.
- Excellent problem‑solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.
Bonus points for:
- Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
- Hands‑on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
- Exposure to streaming data platforms like Kafka.
- Contributions to open‑source ML projects or publications in ML conferences.
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