Playlab is a tech non-profit dedicated to helping educators and students become critical consumers and creators of AI.
We believe that an open-source, community-driven approach is key to harnessing the potential of AI in education. We equip communities with AI tools and hands-on professional development that empowers educators & students to build custom AI apps for their unique context. Over 60,000 educators have published apps on Playlab – and the impact is growing every day.
At Playlab, we believe that AI is a new design material - one that should be shaped by many to bring their ideas about learning to life. If you're passionate about building creative, equitable futures for students and teachers, we hope you’ll join us.
Playlab seeks a Staff Machine Learning Engineer to join our growing Engineering team. As a Staff Machine Learning Engineer you'll be continuously experimenting with emerging AI technologies and translating them into capabilities educators can actually use. You'll work at the intersection of cutting-edge ML and real educational needs - making frontier AI useful, safe, and accessible in educational contexts.
Design and build evaluation systems that assess educational AI quality across thousands of conversations - from learning outcomes to bias detection to curriculum alignment
Build ML systems that enable self-improving app creation - learning from high-quality apps on the platform to automatically scaffold new applications for educators
Design and prototype downloadable, on-device AI models that work without internet connectivity - critical for privacy and global accessibility
Develop systems that enable dynamic, fluid interfaces adapting to learning moments - transitioning seamlessly from chat to writing editor to interactive physics simulation as needed
Build content moderation and safety systems designed specifically for educational discourse
Implement agentic AI systems that enable educators to create goal-directed applications (e.g., "help students through this project over 2 weeks")
Build sophisticated RAG systems that integrate diverse educational content with semantic search and knowledge graphs
And more…
Research, prototype, and implement ML systems that enable educators to build safe, effective AI applications - working with both LLMs and traditional ML models as appropriate
Stay on top of emerging AI research and technologies - evaluate what's relevant for education and integrate what works
Work cross-functionally with engineering, product, and educators to ensure ML systems solve real educational needs
Balance experimentation with production excellence - explore cutting-edge techniques while ensuring reliability, performance, and cost-effectiveness at scale
Contribute to our open-source ML infrastructure and help establish evaluation standards for educational AI
Mentor engineers on ML systems design and implementation through pairing and reviews
7+ years building and deploying ML systems in production, with recent experience in generative AI and LLMs
Strong understanding of ML fundamentals, model fine-tuning, and evaluation methodologies
Experience building production AI systems - you understand latency, cost optimization, and evaluation challenges
Proficient in Python and ML frameworks (PyTorch, TensorFlow, HuggingFace, etc.)
Thrive in high-agency, high collaboration cultures
Great communication that makes working remote-first work
Experience with RAG systems, vector databases, and knowledge graphs
Background in content moderation, safety systems, or bias detection
Contributions to open source ML projects
Experience with model compression or on-device ML
Experience in education or building in edtech
Experience with educational technology or mission-driven organizations
Experience with designing creative platforms
Python, PyTorch/TensorFlow, HuggingFace, OpenAI/Anthropic APIs, AWS Bedrock, Vector Databases (Pinecone/Weaviate), Neo4J, Kubernetes, AWS
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