Responsibilities
- Own large areas of the platform end to end, from design to production deployment.
- Work on knowledge representation systems, including ontologies and knowledge graphs.
- Design and implement RAG pipelines for data processing.
- Build and maintain integrations between retrieval and ML components.
- Develop context retrieval systems balancing recall, precision, latency, and cost.
- Create evaluation frameworks and metrics to measure system performance.
- Build reliable backend services and data pipelines for ML components.
- Deliver experiments and new capabilities quickly with high quality.
Requirements
- 5+ years of experience in building and deploying machine learning or AI systems.
- Master’s or PhD degree in Computer Science, Machine Learning, AI, or equivalent experience.
- Deep understanding of retrieval systems, RAG, embeddings, and knowledge representation.
- Experience with knowledge representation, semantic search, or agentic systems.
- Proficiency in Python, with a focus on production-quality code.
- Experience scaling or shipping products at high-growth startups.
- Ability to operate in ambiguous problem spaces with a balance of research and pragmatism.
- Strong communication skills for cross-functional collaboration.
About Scale AI
Scale’s mission is to develop reliable AI systems for the world’s most important decisions. We provide the high-quality data and full-stack technologies that power the world’s leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. The Scale Generative AI Platform allows customers to build, evaluate, and control advanced AI agents and applications that continuously improve. The Scale Data Engine provides the technology to collect, curate, and annotate high-quality datasets. Through our Scale Labs, we test models with rigorous benchmarks and novel research to ensure breakthroughs translate into systems people can trust. Scale powers the most advanced LLMs and generative models in the world through RLHF, data generation and model evaluation. We work with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force.