Senior Machine Learning Software Engineer, PlayStation

• Educational Background: Bachelor’s degree with 5+ years of related experience
• Coding Proficiency: Demonstrated experience with Java and Python
• AWS Services Competency: Proficiency with common AWS services (EKS/ECS, Kinesis, Lambda, DynamoDB, SNS, SQS)
• Systems Monitoring and Analytics: Experience with systems monitoring, alerting, and analytics tools like Datadog, Splunk, New Relic, or AWS CloudTrail
• Communication Skills: Strong communication skills to collaborate with multi-functional teams
• Container Orchestration: Experience with container orchestration and management (e.g., Kubernetes)
• Data Science & Machine Learning: Basic knowledge of Data Science, Machine Learning, and Large Language Models (LLMs)

Desirable

• This position is a hands-on engineering role with a broad range of responsibilities, from evolving and supporting machine learning/AI pipelines to building low-latency, high-throughput systems.
• You should be well-versed in running applications at internet scale on modern cloud architectures (e.g., AWS) using containerized and serverless workflows.
• If you’re passionate about large-scale machine learning projects, experimentation, automation, or real-time risk mitigation, we’d love to connect with you
• Create Cloud Solutions: Design and implement cloud solutions using AWS to ensure high performance, scalability, and reliability
• Core Infrastructure Development: Develop and maintain core infrastructure for data streaming and processing, applying industry standards and innovative solutions
• ML/AI Pipeline Enhancement: Build and enhance ML/AI pipelines supporting automation, experimentation, continuous integration/delivery, verification/validation, and model monitoring
• Automated Deployment: Ensure all work is deployed in an automated, repeatable fashion, optimizing infrastructure for cost and efficiency
• Service Availability: Guarantee high levels of service availability through being a part of an on-call rotation, following best practices for disaster recovery and business continuity