Descripción de la oferta
Job Description
At King, we create games that are played by millions around the world. To keep raising the bar on quality and player experience, we invest deeply in applied AI/ML — from improving how we build game content to optimising live game decisions at scale.
Your role within the Kingdom
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!
Scope of Work
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
Decision Automation
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
Additional applied ML initiatives
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
Responsibilities
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
Qualifications
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
Technical Environment
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
Benefits
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
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