Teams at Omnifold

Engage with customers to understand operational challenges, frame business problems, conduct technical and value-based demos, and build Python- and SQL-based prototypes. Support evaluations, pilots, implementations, and customer deployments while demonstrating how AI, machine learning, and optimization solve business problems.

Lead and scale a research team from 5 to 20 researchers developing frontier machine learning systems for forecasting, optimization, and control. The role requires strong ML foundations, hands-on experience training and deploying production models, and first- or second-line management experience. Preferred qualifications include LLM infrastructure expertise, quantitative modeling experience, and startup experience. This is an in-person role in San Francisco, five days per week.

Own infrastructure for deploying and operating customer-specific AI models. Responsibilities include production deployments, customer data isolation, security hardening, AWS cloud and GPU resource management, cost optimization, monitoring, logging, ETL pipelines, model versioning, ML lifecycle management, and automated testing. The role requires Python, cloud computing, CI/CD, infrastructure-as-code, security fundamentals, ML workflow familiarity, and a strong computer science background.