Senior Principal AI/ML Engineer

About The Role

We’re looking for a passionate and creative Principal AI/ML Engineer to join the ML Special Projects team, part of King’s AI Center of Excellence (ACE) – a central team that partners with game and shared tech teams to build, ship, and scale machine learning systems that deliver real product impact. As a member of the team, you will be working closely with other AI/ML Engineers, Data Scientists, and Product Managers supporting them to develop and operationalize ML models as part of King’s central AI/ML initiatives. This is a hands‑on, high‑ownership role. You will take problems from discovery and experimentation through to reliable production systems, and help set engineering standards for how ML is built and adopted across King. You are someone who is interested in pushing the boundaries of applied ML in our products and production, improving the experience for over 250 million monthly active users in our games!

What You’ll Work On

• Level, Content & Production Automation

• ML-driven playtesting, quality signals, and simulation to accelerate iteration of content creation and evaluation
• Content evaluation and optimisation to improve the speed, reliability, and scalability of level production workflows
• Where appropriate, reinforcement learning and other sequential or simulation-based approaches to model gameplay and player behavior

• Models and decision policies which improve the experience of our players
• Online learning and experimentation systems (e.g., contextual bandits or similar approaches) with strong safety and evaluation guardrails
• Measurement frameworks that connect proxy metrics to long‑term business and player outcomes

• Representation learning and player modelling on large‑scale event or time-series data to enable downstream use cases
• Foundational ML capabilities, tooling, or services that help product teams adopt and operate ML more effectively
• Exploration of new ML-driven opportunities as games, tools, and business needs evolve

What You’ll Do

• Drive end‑to‑end ML delivery: problem framing → data & features → modelling → evaluation → deployment → monitoring and iteration
• Build and maintain robust pipelines (batch and/or streaming) for training and inference, with strong reproducibility and observability
• Design offline + online evaluation strategies, balancing proxy metrics for game optimisation
• Partner with engineers, data scientists, product managers, and designers across the business to translate opportunities into shippable systems
• Raise the bar on applied ML engineering best practices: reliable releases, clear scoping, defensible trade‑offs, documentation, and maintainable handover
• Provide technical leadership: coach others, influence architecture, and contribute to long‑term ML platform and product strategy

What We’re Looking For (Requirements)

• Proven track record delivering production ML systems end‑to‑end in consumer products or similarly complex environments
• Strong software engineering skills (Python), with experience in modern ML frameworks (e.g., PyTorch/TensorFlow)
• Experience building or operating data/ML pipelines at scale (batch and/or streaming), and working effectively with large datasets
• Solid understanding of experiment design, evaluation and metrics, including how to reason about bias, drift, and measurement pitfalls
• Deep expertise in at least one of the following areas (and willingness to learn others): causal inference, contextual bandits / online learning & decisioning, reinforcement learning / simulation-based evaluation
• Strong operational mindset: CI/CD, infrastructure‑as‑code or equivalent, monitoring/alerting, and debugging in real‑world systems
• Excellent communication, collaboration, and stakeholder management skills: ability to align stakeholders and drive progress across teams
• Strong leadership skills to coach and mentor more junior team members

Nice to have

• Experience building ML tooling/platform capabilities
• Experience in games (mobile, console, casual, or otherwise) and curiosity about how gameplay connects to player experience and spending behaviour
• Contributions to open source or community ML tooling

Our Tech Environment (Examples)

• Python, modern ML stacks (PyTorch/TensorFlow), experiment tracking and evaluation at scale
• Batch and streaming data processing; cloud data platforms and ML infrastructure
• Git‑based workflows, CI/CD, infrastructure‑as‑code, monitoring and observability practices
• Google Cloud, BigQuery, SQL

Why Join

• Work on ML problems that ship into real products, not just prototypes
• Operate at massive scale, with real constraints and real impact
• Influence how ML is built and adopted across multiple teams and domains
• Join a group that values pragmatic engineering, principled measurement, and clear communication

Locations

• Stockholm
• London
• Barcelona