Machine Learning Engineer, PlayStation

• Advanced degree (Master’s or Ph.D.) in CS/Statistics/Data Science/Operations Research/Econometrics, specializing in machine learning and optimization problems
• 1+ years of industry experience applying time-series models in production, with hands-on experience in forecasting libraries (e.g. with LightGBM, Prophet)
• Proven track record with mixed-integer programming or constraint solvers for pricing or resource allocation
• Strong programming skills in Python, Java, with proficiency in ML libraries and frameworks like TensorFlow, PyTorch, Spark Mlib, and scikit-learn
• Good ML systems engineering experience (such as job orchestration, training pipelines, deployment, serving, monitoring)
• Expertise in measuring the performance unsupervised and supervised models
• Proven record of successfully delivering commercial machine learning products from conception to production
• Experience with software engineering principles, modern ML tech stacks, and use of Databricks, Tecton, Snowflake, and cloud services like AWS for machine learning development and deployment

Desirable

• Do you want to join a Machine Learning team committed to enhancing the PlayStation experience for hundreds of millions of users and also game studios? The work we do delivers highly impactful pricing recommendations to our studios and has strong leadership sponsorship. The Machine Learning Engineers within the DTIS group will deliver optimized interactions across PlayStation experiences by designing, coding, training, documenting, cost-effectively deploying, and evaluating business-critical machine learning and AI systems
• We are looking for someone who can architect and build delightful production-grade products for customer-facing experiences, in an agile environment, collaborating with teams across Engineering, Product and Business. You will work on a project backed by executive leadership and poised to significantly influence revenue and user engagement
• Build and maintain ML models that quantify product price-elasticity and cannibalization effect to support multi-period promotions
• Refine time-series forecasting algorithms to predict demand across multiple regions and product lines
• Develop optimization workflows (OR-Tools) that balance sales growth and business constraints
• Refine clustering algorithms to identify lookalike products for smarter pricing and promotional strategies
• Apply a broad spectrum of state-of-the-art machine learning and deep learning technologies in the areas of classification, regression, clustering, time series forecasting, uplift modeling, and synthetic data generation
• Build scalable and resilient ML systems along with appropriate monitoring and continuous evaluation frameworks
• Work closely with cross-functional partners in business and product to translate requirements into effective, scalable, ML-driven solutions
• Collaborate with other engineering functions in Client, Server, and QA to drive ML integrations and deliver results
• Collaborate with ML application teams that power product personalizations and search to further enhance the core experiences of our players