Machine Learning Engineer, PlayStation

• Advanced degree (Master’s or Ph.D.) in CS/Statistics/Data Science, specializing in machine learning
• Industry work experience in designing and implementing large-scale search retrieval, ranking models
• Proven track record of successfully delivering large-scale commercial machine learning products from conception to production
• Experience with Python, and ideally also Scala or Java, proficient with ML frameworks such as Tensorflow, PyTorch, MLlib
• Experience in developing a variety of ML models using structured and unstructured data
• Industry experience in A/B testing
• Strong communication skills, both verbal and written, with the ability to convey complex technical concepts to stakeholders with limited knowledge of ML/AI
• Experience with software engineering principles, modern ML tech stacks, and use of Databricks, Snowflake, and cloud services like AWS for machine learning development and deployment
• Excellent problem-solving skills, with the ability to navigate through ambiguity to drive results

Desirable

• We deliver impactful ML models and systems that serve an increasingly dynamic and personal experience!
• The Machine Learning Engineers within the DTIS group will deliver optimized interactions across PlayStation experiences and systems by designing, coding, training, deploying, and evaluating very large-scale machine learning systems
• We are looking for someone who can build delightful products and experiences for millions, in an agile environment, collaborating with teams across Engineering, Product, and Business
• Further, you will be immersed in groundbreaking ML technologies, tools and processes, as you help to advance our technical objectives and architectural initiatives
• Design and develop various machine learning and deep learning models and systems for high-impact consumer-facing applications such as search retrieval and ranking, content personalizations, and product recommendations
• Work with a broad spectrum of state-of-the-art machine learning and deep learning technologies in the areas of recommendation systems
• Create metrics and design A/B test plans to evaluate model performance offline and online to inform and convey our impacts to diverse groups of stakeholders
• Collaborate with cross-functional teams of technical members and non-technical members in architecture, design, and code implementations
• Analyze and produce insights from a large amount of dynamic structured and unstructured data using modern big data technologies
• Produce reusable code and conduct peer code reviews according to standard methodologies in Python