Job
Description
Build the AI Reasoning Layer for Education We’re reimagining the core intelligence layer for education —tackling one of the most ambitious challenges in AI: subjective assessment automation and ultra-personalized learning at scale. This isn’t just another LLM application. We’re building a first-principles AI reasoning engine combining multi-modal learning, dynamic knowledge graphs, and real-time content generation . The goal? To eliminate billions of wasted hours in manual evaluation and create an AI that understands how humans learn . As a Founding AI Engineer , you’ll define and build this system from the ground up. You’ll work on problems few have attempted, at the bleeding edge of LLMs, computer vision, and generative reasoning. What You’ll Be Solving: Handwriting OCR at near-human accuracy: How can we push vision-language models to understand messy, real-world input from students? Real-time learner knowledge modeling: Can AI track and reason about what someone knows—and how they’re learning—moment to moment? Generative AI that teaches: How do we create dynamic video lessons that evolve in sync with a learner’s knowledge state? Scalable inference infrastructure: How do we optimize LLMs and multimodal models to support millions of learners in real time? What You’ll Be Building: Architect, deploy & optimize multi-modal AI systems—OCR, knowledge-state inference, adaptive content generation. Build reasoning engines that combine LLMs, retrieval, and learner data to dynamically guide learning. Fine-tune foundation models (LLMs, VLMs) and implement cutting-edge techniques (quantization, LoRA, RAG, etc.). Design production-grade AI systems: modular, scalable, and optimized for inference at global scale. Lead experiments at the frontier of AI research, publishing if desired. Tech Stack & Skills Must-Have: Deep expertise in AI/ML, with a focus on LLMs, multi-modal learning, and computer vision. Hands-on experience with OCR fine-tuning and handwritten text recognition Strong proficiency in AI frameworks: PyTorch, TensorFlow, Hugging Face, OpenCV. Experience in optimizing AI for production: LLM quantization, retrieval augmentation, and MLOps. Knowledge graphs and AI-driven reasoning systems experience Nice-to-Have: Experience with Diffusion Models, Transformers, and Graph Neural Networks (GNNs). Expertise in vector databases, real-time inference pipelines, and low-latency AI deployment. Prior experience in ed-tech, adaptive learning AI, or multi-modal content generation. Why This Role Is Rare Define the AI stack for a category-defining product at inception. Work with deep ownership across research, engineering, and infrastructure. Founding-level equity and influence in a high-growth company solving a $100B+ problem. Balance of cutting-edge research and real-world deployment. Solve problems that matter —not just academically, but in people’s lives. Who this role is for This is for builders at the edge—engineers who want to architect, not just optimize. Researchers who want their ideas shipped.If you want to: Push LLMs, CV, and multimodal models to their performance limits. Build AI that learns, reasons, and adapts like a human tutor. Shape the foundational AI layer for education Show more Show less
