About the role
WHAT YOU 'LL DO• Build AI models — fine-tune and deploy LLMs, embedding models, and classical ML to power tutoring, recommendations, assessments, and content generation across our Ed-Tech products.• Prototype to production — translate fuzzy product ideas into working prototypes in days, then harden them into reliable services with proper evals, guardrails, and monitoring.• AI-native engineering — use Claude Code, Codex, and Cursor as daily drivers to design, refactor, and test code; orchestrate agents and tool-use for non-trivial workflows.• Retrieval & agents — design RAG pipelines, vector search, and agentic systems over our learning content, course catalogs, and user signals.• Evals & quality — build evaluation harnesses, golden sets, and A/B experiments to keep model quality measurable and improving.• Collaborate cross-functionally — partner with product, design, and content teams to scope problems, define success metrics, and ship learner-facing impact.
MUST HAVE• 2–6 years building and shipping AI / ML systems in production (not just notebooks).• Hands-on fluency with AI-powered dev tools — Claude Code, Codex, Cursor — and the judgment to know when to lean on them vs. write it yourself.• Strong Python; solid grasp of modern LLM stacks (OpenAI, Anthropic, Hugging Face, LangChain / LlamaIndex or equivalents).• Experience with prompt engineering, fine-tuning, RAG, embeddings, and at least one vector DB (pgvector, Pinecone, Weaviate, Qdrant).• Comfort with cloud (AWS / GCP / Azure), Docker, and basic MLOps — model serving, versioning, observability.• Bachelor's in CS, ML, or related field; bias for shipping, strong written communication, and clear thinking under ambiguity.
BONUS POINTS• Built or shipped AI features in Ed-Tech, data science, or analytics platforms.• Experience with multi-agent systems, MCP, tool-use, or evaluation frameworks (e.g., Inspect, Promptfoo, Ragas).• Open-source contributions, public AI projects, or technical writing on LLMs and applied ML.WHY JOIN USShape AI products used by millions of learners · high ownership with product & leadership · fast, learning-driven culture · competitive compensation and growth.Apply now and help build the future of AI-native learning