Staff ML Infrastructure Engineer

Core responsibilities:

• Design, build, and maintain production ML infrastructure that balances performance, cost, and reliability
• Build data pipelines that scrub PII, create research datasets, and power the research portal for educational AI studies
• Architect the path toward self-hosted and on-device model deployments for privacy and global accessibility
• Design and implement model orchestration systems that intelligently route requests across multiple AI providers

This role creates positive change by designing systems that keep AI accessible as Playlab grows, balancing cutting-edge capabilities with cost efficiency, powering research into what works in educational AI, and building toward a future where sophisticated AI can run anywhere in the world to help educators and students become critical consumers and creators of AI.

Required qualifications:

• 7+ years building production ML/data systems, with experience in ML operations and infrastructure
• Strong experience with model serving, orchestration, and optimization in production environments
• Proficient in Python and data pipeline technologies (Airflow, ETL tools, etc.)
• Experience with cloud infrastructure (AWS preferred) and containerization (Kubernetes, Docker)

No specific benefits, compensation, or perks mentioned in the job description.