Staff Machine Learning Engineer

London, United Kingdom

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:

• Lead the design, develop and deploy various machine learning and deep learning models and systems for high impact consumer and enterprise applications  ranging from content and experience personalizations, search ranking, online safety, to AI agents, 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 recommendation systems, natural language processing, reinforcement learning, time series forecasting, as well as generative AI such as LLMs.
• Architect and lead the implementation of 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
• Mentor and guide junior machine learning engineers, providing technical leadership and fostering a culture of continuous learning and improvement.
• 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 CS/Statistics/Data Science, specializing in machine learning or equivalent experience.
• Strong programming skills in languages such as Python, Java, or Scala, with proficiency in machine learning libraries and frameworks like TensorFlow, PyTorch, Mlib, and others.
• Proven track record of successfully delivering large-scale commercial machine learning products from conception to production, in technical leadership roles. 
• Expertise in a variety of machine learning and AI techniques, such as supervised and unsupervised learning, deep learning, reinforcement learning, natural language processing, computer vision.
• Experience with the latest industry knowledge in Generative AI technology with the ability to formulate GenAI specific use cases and implementations that are optimized for cost and scalability. 
• 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.
• Strong communication skills, both verbal and written, with the ability to convey complex technical concepts to stakeholders with little prior-knowledge on ML/AI.
• Excellent problem-solving skills, with the ability to navigate through ambiguity to drive results.

Preferred Qualifications:

• Experience in building and maintaining large scale online customer-facing 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 for continuous integration, deployment, and monitoring of machine learning models.
• Familiarity with ethical considerations and best practices in machine learning, including fairness, accountability, and transparency 

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