Machine Learning Engineer

Job Description

Machine Learning Engineer

Location: San Francisco

Do you want to join a Machine Learning team committed to personalizing the PlayStation experience for millions of users? The work we do delivers impactful insights to build an increasingly dynamic and interactive experience! The Machine Learning Engineer within the platform engineering group will deliver optimize interactions across PlayStation experiences and systems by designing, coding, training, documenting, cost-effectively 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 Design. Further, you will be immersed in ML technologies, tools and processes, as you help to advance our technical objectives and architectural initiatives.

Responsibilities:
• Design and develop supervised classification, ranking, and recommendation systems.
• Evaluate model performance online and offline.
• Prototype production-ready data science solutions for business problems.
• Identify and extract information from huge amounts of structured and unstructured data.
• Collaborate with technical and non-technical team members.
• Ability to code effectively in Python, Scala or Java

Qualifications:
• 3+ years of industry experience in designing and analyzing machine learning-based solutions that ideally include: recommender systems, personalization, forecasting, conversational systems, outlier detection, and hypothesis testing.
• Strong Statistical knowledge
• MS in Engineering, data science with a specialization in machine learning or equivalent experience

Preferred qualifications:
• Experience with tech stacks such as Spark, Kubernetes, Jenkins, Prometheus.
• Experience with machine learning tools such as MLlib, Tensorflow, and scikit-learn.
• Experience system distributed cloud-based environments, such as AWS.

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Jobcode: Reference SBJ-re8465-18-216-190-167-42 in your application.