Senior Machine Learning Engineer - Applied ML & Research

As a Senior 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.

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:

• Partner with product and engineering to identify and execute machine learning use cases that deliver measurable impact


• Design, build, and iterate on machine learning solutions (e.g., classifiers, regressors, ranking/retrieval, and rule-based components)


• Contribute across the ML lifecycle: data exploration, feature engineering, training, evaluation, deployment, and monitoring


• Implement reliable training/inference pipelines and help improve reproducibility, testing, and observability


• Communicate model behavior, trade-offs, and results clearly to both technical and non-technical stakeholders


• Contribute to team standards: code quality, documentation, experimentation hygiene, and responsible ML practices

We're looking for someone with:

• Bachelor’s degree in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field (Master’s a plus)


• 4+ years of industry experience building and deploying ML systems


• Solid proficiency in Python and familiarity with common ML libraries (e.g., PyTorch, XGBoost) and SQL


• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies


• Demonstrated ability to write maintainable, tested code, participate in code reviews, and follow engineering best practices


• Strong problem-solving skills with the ability to break down ambiguous problems into scoped tasks and deliver iteratively

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.