Head of Research
Omnifold trains frontier models for forecasting and optimization. Our research team is professors and PhDs from OpenAI, Adept, Google, Stanford, and MIT. We build systems to outperform the state of the art algorithms in prediction, optimization, and control, with a focus on supply chain use cases. We are looking for a leader with the following background:
Must have
Background in ML foundations (i.e. PhD in CS theory, statistics, econometrics, operations research / systems engineering, math, or theoretical physics)
Hands on experience training and deploying production ML models
First-line or second line management experience minimum (remit is to scale a team from 5-20 researchers)
Nice to have
Experience with LLM infrastructure - inference, fine-tuning, RL etc.
Success in environments with quantitive models (quant research fund, ranking, ads)
Startup experience
Location:
San Francisco (in-person, 5 days per week)
Omnifold’s Mission
Every bad forecast has a physical consequence. Unnecessary goods are manufactured, shipped, and stored. Emergency air freight is needed for misallocated products. Poor production planning means workers show up with nothing to do, or work frantic overtime. Inefficiency is everywhere.
Our mission is to eliminate waste and accelerate growth for every company with physical products.
Skills Required
- Background in machine learning foundations, with a PhD in computer science theory, statistics, econometrics, operations research, systems engineering, mathematics, or theoretical physics
- Hands-on experience training and deploying production machine learning models
- First-line or second-line management experience
- Experience with LLM infrastructure, including inference, fine-tuning, or reinforcement learning
- Success working with quantitative models, such as in quantitative research, ranking, or advertising
- Startup experience
What We Do
Omnifold develops AI-powered planning software for complex supply chains and commercial operations. Its platform combines internal, external, and customer-specific data to model procurement, manufacturing, distribution, marketing, and sales dynamics, then uses prediction, optimization, language, and reasoning to improve forecasting. The system adapts to market changes and discovers growth strategies, helping businesses improve margins, cash flow, and operational decisions across complex enterprises.
