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Omnifold trains frontier models for forecasting and optimization, with a research team composed of professors and PhDs from OpenAI, Adept, Google, Stanford, and MIT. The company builds systems to outperform state-of-the-art algorithms in prediction, optimization, and control, with a focus on supply chain use cases.
As Head of Research, you will lead and scale the research organization from 5 to 20 researchers, setting the technical direction and strategy for the team. You will be responsible for advancing Omnifold's core capabilities in ML-based forecasting and optimization, ensuring research efforts translate into production systems that deliver measurable value to customers in supply chain management.
Omnifold's mission is to eliminate waste and accelerate growth for companies with physical products. The company addresses a critical problem: every bad forecast has cascading physical consequences—unnecessary goods are manufactured, shipped, and stored; emergency air freight is needed for misallocated products; poor production planning leads to worker inefficiency. The role offers the opportunity to lead cutting-edge research that directly impacts operational efficiency at scale.
You will work in-person at the San Francisco headquarters, 5 days per week, in a research-driven environment focused on quantitative modeling and production ML systems.
Requirements:
- PhD in CS theory, statistics, econometrics, operations research, systems engineering, mathematics, or theoretical physics
- Hands-on experience training and deploying production ML models
- First-line or second-line management experience (minimum); demonstrated ability to scale and lead research teams
Nice to have:
- Experience with LLM infrastructure (inference, fine-tuning, reinforcement learning, etc.)
- Success in quantitative modeling environments (quant research funds, ranking systems, ads)
- Startup experience
