Schemata is a spatial intelligence company that is building the world's AI interface. Immersive, AI-native training built for the speed and scale of modern operations. Backed by a16z speedrun.
About the role
We are seeking a highly skilled LLM Platform Engineer to join our team full‑time. You will play a foundational role in designing, building and optimizing the AI systems that turn heterogeneous knowledge — technical documentation, video, imagery, voice, and 3D scene data — into grounded, real‑time guidance for next‑generation training and maintenance applications.
What they're looking for
- 4+ years of software engineering experience, with at least 1–2 years building production LLM systems (RAG pipelines, agents, or LLM‑powered products with real users)
- Hands‑on experience designing knowledge ingestion and retrieval systems: embedding models, vector and hybrid search, chunking strategies, and semantic data modeling across more than one modality
- Strong Python and backend engineering fundamentals: APIs, data pipelines, async systems, and working in a cloud environment (AWS preferred)
- A metrics‑driven approach: experience building evals or benchmarks for LLM systems and using them to drive iteration, not just report scores
- Demonstrated ability to take ambiguous problems from prototype to reliable, maintainable production services
- Experience deploying and operating production systems: CI/CD, release management, and monitoring
More about this role
At Schemata, we are transforming the $400 B virtual‑training and simulation market by fusing 3D computer vision, neural rendering and large multimodal models inside highly regulated industries. Our platform delivers photorealistic, intelligent 3D experiences, demanding robust spatial reasoning, high‑performance data pipelines and seamless integration between traditional graphics and AI‑driven perception.
We are seeking a highly skilled LLM Platform Engineer to join our team full‑time. You will play a foundational role in designing, building and optimizing the AI systems that turn heterogeneous knowledge — technical documentation, video, imagery, voice, and 3D scene data — into grounded, real‑time guidance for next‑generation training and maintenance applications.
This is a high‑impact, cross‑functional role: you will work end‑to‑end from evaluating emerging methods to production inference and performance optimization, ensuring our platform retrieves the right knowledge, reasons over it reliably, and responds in real time across diverse deployment environments.
Design and build multimodal knowledge base ingestion: pipelines that convert documents, video, images, audio, and...
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