Senior Applied MLE at Series A AI context infrastructure startup

You will build the semantic layer essential for the next generation of autonomous enterprise agents. By extracting knowledge from fragmented data and constructing real-time AI Data Graphs, you’ll solve the context gap that causes 95% of AI pilots to fail, working directly with a world-class team of industry pioneers.

• Design and deploy state-of-the-art models to extract structured domain knowledge from heterogeneous and disconnected enterprise data sources.
• Develop, evaluate, and scale AI Data Graphs and semantic systems that provide the reasoning foundation for high-precision autonomous agents.
• Fine-tune large and small language models (LLMs/SLMs) with domain-specific context to ensure production-grade accuracy and reduced inference costs.

• Holds an MS or PhD in Computer Science or ML with 4+ years of hands-on experience in NLP, knowledge extraction, or retrieval.
• Demonstrates a proven track record of shipping and maintaining ML models in production environments, specifically using BERT, LLMs, or embedding-based retrieval.
• Possesses a builder mindset with previous startup experience, thriving in fast-paced environments where they can influence core infrastructure from day one.

• Work under a founding team featuring the former Chief Architect of Visa’s AI Platform and the architect of LinkedIn’s massive production Skills Graph.
• Solve the industry's most pressing bottleneck—enterprise domain intelligence—by building the infrastructure that sits between raw data lakes and autonomous agents.
• Join a high-growth Series A startup already deploying with Fortune 500 leaders across the insurance, travel, and technology sectors.