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Senior Machine Learning Engineer

Supercell
CompanySupercell
CategoryEngineering
LocationHelsinki
RemoteOn-site (inferred)
EmploymentNot stated
LevelSenior
SalaryNot stated by the employer
Posted15 Jun 2026
Last verified30 Jul 2026
SourceEmployer career page (ashby)
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Description
[https://app.ashbyhq.com/api/images/user-content/1246dead-d30e-4c70-a1d3-ef7e5023d542/13483750-170e-4f68-ba7d-ff5ae217444b/hog%20rider%202.0.jpeg] WE’RE LOOKING FOR A SENIOR MACHINE LEARNING ENGINEER WHO CAN BUILD LARGE-SCALE SYSTEMS AND TURN THEM INTO IMPACTFUL SOLUTIONS FOR PLAYERS AND THE BUSINESS. IF DESIGNING REAL-TIME, PLAYER-FACING USE CASES WITH MEASURABLE IMPACT AT MASSIVE SCALE SOUNDS EXCITING, THIS COULD BE A GREAT FIT. Supercell is rapidly scaling its machine learning capabilities. We are building a scalable ML platform and bringing personalized player experiences into production at massive scale. Our ambition is to empower game teams to deliver billions of automated, data-driven decisions across the full player journey every day. You’ll join a small, high-impact team of Data Science and Machine Learning experts, working closely with game teams, LiveOps, Data, and Analytics. You’ll play a key role in shaping Supercell’s ML platform, delivering impactful solutions, and enabling teams to build and scale ML-driven features. Your work will span the full ML lifecycle from business problem formulation and data collection to modeling and deployment real-time decisioning, helping turn ideas into production systems that directly impact players. You’ll set strong engineering practices, share your knowledge, and help shape how we use ML to optimize player experiences. [https://app.ashbyhq.com/api/images/user-content/1246dead-d30e-4c70-a1d3-ef7e5023d542/7a053edc-bf2e-4db4-85ee-5630e2b5d305/banner_narrow_customs.jpg] WHAT YOU'LL BE DOING - Understand our games, players, and business goals to identify where ML can deliver meaningful impact, and build strong relationships across teams. - Design and build end-to-end, real-time and event-driven ML systems from data to low-latency serving, enabling context-aware player experiences. - Develop and deploy ML models and decision systems that shape personalized player experiences across the full player journey. - Build scalable ML systems and reusable platform capabilities, enabling game teams to adopt ML through tools, pipelines, and APIs. - Balance speed, robustness, experimentation, and standardization while continuously reducing time from idea to experiment to production. - Operate, monitor, and optimize production ML systems to ensure world-class performance, reliability, and scalability. - Champion modern MLOps practices through mentorship, code and design reviews, documentation, and by guiding game teams in adopting ML workflows. - Shape ML strategy and ensure it drives real player and business impact. - Continue to level up Supercell AI maturity throught knowledge sharing and adoption of new tools. WHAT YOU HAVE - Deep expertise in Machine Learning with a strong track record of taking ML systems into production. - Hands-on experience deploying ML models at large scale, operating in high-traffic, low-latency production environments. - Strong software, data, and ML engineering skills, with the ability to design, build, and maintain robust end-to-end ML pipelines. - Experience leveraging modern AI-assisted development tools to improve productivity and accelerate iteration speed. - Proven ability to collaborate with other developers and AI agents, as well as independently drive ML projects from idea to production. - Excellent problem-solving ability, with strong analytical thinking and attention to detail. - Clear and effective communication skills, able to translate complex technical concepts for both technical and non-technical audiences. - Positive, proactive, and “can-do” mindset, with a willingness to dive deep and take ownership. - Passion for games. Previous industry experience is a strong plus. WHAT YOU'LL BE USING The following list is not intended as a strict set of requirements, but rather as examples of relevant technologies and areas of experience - Data & Processing: Python, Databricks, SQL, Spark, Red
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