Staff Machine Learning Engineer

About Playlab

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.

The Role

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.

Examples of the work

• 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…

Expectations

• 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

Qualifications

• 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

Bonus Points For...

• 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

Technologies

Python, PyTorch/TensorFlow, HuggingFace, OpenAI/Anthropic APIs, AWS Bedrock, Vector Databases (Pinecone/Weaviate), Neo4J, Kubernetes, AWS