Staff Machine Learning Engineer - Applied ML & Research
Description
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.
Responsibilities
- 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.
Requirements
- 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.
Benefits
- 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.
