About the Role

Role Overview

As a Senior Machine Learning Engineer, you'll significantly advance our machine learning, directly impacting high-profile mobile games. You'll research, prototype, develop, and deploy ML models to optimize in-game experiences like dynamic difficulty, LTV prediction, and smart ad targeting. Working in an agile team, you'll translate data into scalable solutions that boost player engagement and business performance.

This role offers a unique chance to work on impactful projects across major games, with technology autonomy. Your visible contributions will shape the future of ML here. With models already live, you'll continuously refine our AI features.

We seek a proactive, curious individual comfortable driving end-to-end projects. You need solid production ML experience, including deployment and pipeline development, plus confidence in communicating technical concepts. A passion for gaming and innovation in a collaborative setting are crucial.

Key Responsibilities:

• Research, prototype, develop, and optimize ML models for game performance and player experience, exploring cutting-edge algorithms.
• Collaborate closely with the ML team to deliver scalable, production-ready solutions.
• Research, prototype, develop, and optimize ML models for game performance and player experience, exploring cutting-cutting-edge algorithms.
• Manage and enhance ML pipelines, including data processing, training, and deployment.
• Tune hyperparameters and experiment with features to maximize model accuracy and impact.
• Design, develop, and evaluate A/B tests to measure model effectiveness and optimize player experience.
• Proactively explore data to uncover actionable insights.
• Support deployment processes using CI/CD tools and cloud platforms (AWS/Google Cloud).
• Maintain and improve existing ML systems for robustness, scalability, and efficiency.
• Align ML solutions with business goals by working alongside product managers, developers, analysts, and QA.
• Clearly communicate model results and insights to stakeholders for data-driven decisions.

Skills, Knowledge & Expertise

• Strong proficiency in Python and SQL for data manipulation and model development.
• Solid understanding of ML theory and practical application.
• Strong understanding of statistical concepts (e.g., hypothesis testing, sampling, probability distributions).
• Experience with ML libraries (e.g., scikit-learn; TensorFlow or PyTorch a plus).
• Proven track record of deploying ML models to production environments.
• Experience with software development concepts including Version Control (e.g., Git), CI/CD pipelines, and containerization tools like Docker and Kubernetes.
• Experience with cloud platforms (AWS/Google Cloud) for scalable model deployment.
• Ability to work independently and manage end-to-end ML projects.
• Experience with recommendation systems, reinforcement learning, or multi-armed bandits highly desirable.
• Comfortable collaborating to translate business needs into technical solutions.
• Strong problem-solving skills with a proactive and curious approach to data exploration and model improvement.
• Experience working in a team environment, effectively sharing experiments and results.
• Ability to communicate complex technical concepts clearly to non-technical stakeholders, effectively bridging the gap between technical and non-technical teams.
• Good command of analytical programming and visualization libraries (e.g., R, Matplotlib, ggplot) and supporting tools (e.g., Sagemaker, VS Code).