Machine Learning Engineer - Recommender Systems

Machine Learning Engineer, Recommendation System

Do you want to join a Machine Learning team committed to personalizing the PlayStation experience for hundreds of millions of users? 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.

You Will:

• Design and develop various machine learning and deep learning models and systems for high-impact consumer-facing applications such as 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.

You Bring:

• Masters degree in CS/Statistics/Data Science, with a specialization in machine learning.
• Experience with Python, and ideally also Scala or Java.
• Experience in developing ML models for structured data.
• Proficient with Machine learning frameworks such as Tensorflow, PyTorch, Spark MLlib.
• Industry experience in designing and implementing large-scale machine learning-based recommender systems.
• Industry experience in A/B testing, experimentation design and test analysis.
• Strong coding, machine learning, and statistical knowledge.

Preferred Qualifications:

• Experience with Databricks, Snowflake, Spark, Tecton, Kubernetes, Helm, Jenkins.
• Familiarity with standard methodologies in large-scale DL training/Inference.
• Experience in cloud-based environments, such as AWS.
• Experience working with custom ML platforms.
• Experience with LLM applied in recommendation systems.
• Proactive self-starter with experience working in fast-paced environments.