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2 months ago
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About PlaylabPlaylab 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.The RolePlaylab 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.Examples of the workDesign and build evaluation systems that assess educational AI quality across thousands of conversations - from learning outcomes to bias detection to curriculum alignmentBuild ML systems that enable self-improving app creation - learning from high-quality apps on the platform to automatically scaffold new applications for educatorsDesign and prototype downloadable, on-device AI models that work without internet connectivity - critical for privacy and global accessibilityDevelop systems that enable dynamic, fluid interfaces adapting to learning moments - transitioning seamlessly from chat to writing editor to interactive physics simulation as neededBuild content moderation and safety systems designed specifically for educational discourseImplement 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 graphsAnd more...ExpectationsResearch, prototype, and implement ML systems that enable educators to build safe, effective AI applications - working with both LLMs and traditional ML models as appropriateStay on top of emerging AI research and technologies - evaluate what's relevant for education and integrate what worksWork cross-functionally with engineering, product, and educators to ensure ML systems solve real educational needsBalance experimentation with production excellence - explore cutting-edge techniques while ensuring reliability, performance, and cost-effectiveness at scaleContribute to our open-source ML infrastructure and help establish evaluation standards for educational AIMentor engineers on ML systems design and implementation through pairing and reviewsQualifications7+ years building and deploying ML systems in production, with recent experience in generative AI and LLMsStrong understanding of ML fundamentals, model fine-tuning, and evaluation methodologiesExperience building production AI systems - you understand latency, cost optimization, and evaluation challengesProficient in Python and ML frameworks (PyTorch, TensorFlow, HuggingFace, etc.)Thrive in high-agency, high collaboration culturesGreat communication that makes working remote-first workBonus Points For…Experience with RAG systems, vector databases, and knowledge graphsBackground in content moderation, safety systems, or bias detectionContributions to open source ML projectsExperience with model compression or on-device MLExperience in education or building in edtechExperience with educational technology or mission-driven organizationsExperience with designing creative platformsTechnologiesPython, PyTorch/TensorFlow, HuggingFace, OpenAI/Anthropic APIs, AWS Bedrock, Vector Databases (Pinecone/Weaviate), Neo4J, Kubernetes, AWS
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