10 days ago
Dublin, IrelandSenior
Responsibilities
- Partner with ML scientists, data engineers, central technology teams, and game studios to deploy production-grade ML decision services.
- Own and mature MLOps practices including model CI/CD, model registries, feature stores, real-time inference, monitoring, drift detection, and reproducibility.
- Design and prototype ML-powered products for recommendations, matchmaking, cheat and toxicity intervention, and economy balancing.
- Plan and execute the integration of ML applications into games and live products on launch timelines.
- Set technical direction, mentor engineers, and promote engineering standards across the ML community.
- Identify, evaluate, and pilot GenAI and LLM opportunities, translating successful concepts into secure, scalable, measurable solutions.
Requirements
- Bachelor’s degree in Computer Science, Computer/Electrical Engineering, or a related STEM field plus 4+ years of software development or engineering experience, including substantial production ML deployment experience; alternatively, a master’s degree with 2–4 years of relevant experience.
- Strong programming skills with proficiency in Python; familiarity with C, C++, Java, or Rust is a plus.
- Strong understanding of supervised and unsupervised learning, common traditional ML and deep learning algorithms, and the end-to-end ML lifecycle; reinforcement learning experience is beneficial.
- Hands-on experience with PyTorch, TensorFlow, scikit-learn, or Spark ML.
- Production experience with cloud infrastructure, containers, orchestration, serverless systems, and microservice architectures.
- Hands-on experience with MLOps, model registries, feature stores, pipeline orchestration, automated training, deployment, and monitoring.
- Experience with relational and NoSQL databases and lakehouse or big-data technologies such as Apache Spark or Databricks/Delta.
- Preferred experience with recommender systems, search algorithms, matchmaking, reinforcement learning, infrastructure as code, managed ML platforms, stream processing, GenAI/LLM application patterns, vector databases, Unreal, or Unity.
- Ability to collaborate with the US headquarters through flexible late-start or late-end hours, with core hours approximately 10:00–18:30 local time.
Benefits
- Hybrid work arrangement, with flexibility to start or end late to provide overlap with the US headquarters.
- Equal opportunity workplace committed to diversity, inclusion, and reasonable accommodation for qualified individuals with disabilities.
Tech Stack
Apache AirflowApache KafkaApache SparkAWSAzureCC++DatabricksGoogle Cloud PlatformJavaKubernetesMLflowPythonPyTorchRustscikit-learnTensorFlowTerraformUnity
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