• Architect the video generation pipeline end to end. • Design the gen-AI pipeline that transforms lesson specifications into storyboards, scene graphs, scripts, and production plans. • Ship multiple substantial features per week. • Build the multi-agent production workflow. • Engineer the video generation pipeline. • Design programmatic motion to support worked examples with narration. • Run parallelized rendering with generative video models. • Build the ground-truth quality system. • Architect resilient, high-scale media infrastructure. • Raise the bar for the team.

• Develop and experiment with LLMs • Develop REST APIs using Python and FastAPI • Work with AI-assisted and agentic coding tools • Build, deploy, and secure MCP servers • Understand and apply multi-agent systems for problem-solving • Design RAG systems including vector databases and semantic search • Write prompts for various use cases • Develop generative solutions and integrate them into production • Optimize backend systems and implement security best practices • Evaluate LLM systems and ensure quality metrics are met

• Design and build AI-enabled features for various education products, from assessments to tutoring. • Work with education partners and internal stakeholders to understand assessment practices, quality expectations and real-world constraints. • Build evaluation approaches that measure output quality, consistency, usefulness and failure modes. • Develop retrieval, prompt-management, orchestration and structured-output workflows where appropriate. • Integrate LLM APIs, AI services and education data into reliable product capabilities. • Design practical human-review, control and escalation workflows for AI-supported decisions. • Improve the reliability, latency, cost and quality of AI services in production. • Build prototypes quickly, test them against real education use cases and productionise successful approaches. • Develop suitable guardrails, monitoring and auditability for AI outputs. • Work with Backend Engineers on service architecture, data handling, integrations and scalability. • Work with Product Engineers to ensure AI features are understandable, useful and trustworthy for teachers and students. • Use AI-assisted development tools appropriately while maintaining strong engineering judgement, testing, privacy and security standards. • Write clean, tested and maintainable code.

• Design and build AI-enabled features for various education products, from assessments to tutoring. • Work with education partners and internal stakeholders to understand assessment practices, quality expectations and real-world constraints. • Build evaluation approaches that measure output quality, consistency, usefulness and failure modes. • Develop retrieval, prompt-management, orchestration and structured-output workflows where appropriate. • Integrate LLM APIs, AI services and education data into reliable product capabilities. • Design practical human-review, control and escalation workflows for AI-supported decisions. • Improve the reliability, latency, cost and quality of AI services in production. • Build prototypes quickly, test them against real education use cases and productionise successful approaches. • Develop suitable guardrails, monitoring and auditability for AI outputs. • Work with Backend Engineers on service architecture, data handling, integrations and scalability. • Work with Product Engineers to ensure AI features are understandable, useful and trustworthy for teachers and students. • Use AI-assisted development tools appropriately while maintaining strong engineering judgement, testing, privacy and security standards. • Write clean, tested and maintainable code.