Machine Learning Engineer II

Mid-Level Machine Learning Engineer

London, UK

Do you want to join a Machine Learning team committed to enhancing the PlayStation experience for hundreds of millions of users and creators? The work we do delivers impactful insights to build an increasingly dynamic and interactive experience! The Machine Learning Engineers within the platform engineering group will deliver optimized interactions across PlayStation experiences and systems by designing, coding, training, documenting, cost-effectively deploying and evaluating very large-scale machine learning and AI systems.

We are looking for someone who can build delightful products and experiences for millions, in an agile environment, collaborating with teams-across Engineering and Product. Further, you will be immersed in groundbreaking ML/AI technologies, tools and processes, as you help to advance our technical objectives and architectural initiatives.

You Will:

• Design, implement and deploy various machine learning and deep learning models and systems for high impact consumer and enterprise applications ranging from online safety detections, LLM-enabled agents, to Computer-vision-driven operation automation, decision optimization and more.
• Work with a broad spectrum of state-of-the-art machine learning and deep learning technologies, in the areas of Computer Vision(CV), Natural Language Processing(NLP), Reinforcement Learning, Time Series Forecasting, as well as Generative AI such as Large Language Models.
• Implement the full machine learning development cycle, including data collection, preprocessing, feature engineering, model training, evaluation, and deployment, and conduct rigorous testing and validation to ensure accuracy, efficiency, and scalability
• Create metrics to evaluate model performance and help the tech lead convey our impacts to product and business partners
• Stay current with the latest advancements in machine learning and AI research and technology, and integrate relevant innovations into our products and services.
• Work as ML/AI technical lead in complex product development effort, interfacing and collaborating with leaders from cross-functional teams, and representing ML/AI team in communication with key stakeholders and/or senior leadership.
• Thought-lead and influence technology strategy and product roadmap through ML/AI lens, contributing into the development and execution of overall ML/AI strategy.

You Bring:

• Advanced degree (Master’s or Ph.D.) in Computer Science/Statistics/Data Science, specializing in machine learning or equivalent experience
• Strong programming skills in languages such as Python, Java, Scala, with proficiency in machine learning libraries and frameworks like TensorFlow, PyTorch, PySpark, etc
• Proven track record of successfully delivering large-scale commercial machine learning products from conception to production
• Excellent problem-solving skills and comfortable with ambiguity in projects
• Expertise in a variety of machine learning algorithms, such as supervised and unsupervised learning, reinforcement learning, NLP, and computer vision
• Hands-on experience with applying Generative AI technology to solve business problem
• Experience with software engineering principles, big data stacks such as spark, and use of cloud services like AWS, or GCP for machine learning development and deployment
• Effective communication to convey ML concepts to non-tech stakeholders

Preferred Qualifications:

• Experience in Databricks, Snowflake, Seldon, mlFlow, PySpark, Kubeflow, Tecton
• Hands-on experience in vector databases, RAG, open-source LLM models
• Experience in building large-scale online customer-facing ML inference microservices
• Experience with large-scale unstructured data (e.g., audio, images, documents, etc.) manipulation and processing
• Contributions to the machine learning community through publications, open-source projects, or presenting in conferences and workshops
• Experience working with custom ML platforms and monitoring of ML models
• Familiarity with ethical considerations and best practices in machine learning, including fairness, accountability, and transparency

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