Machine Learning Engineer
Role details
Job location
Tech stack
Job description
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 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
Requirements
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.
