The AI planning platform purpose-built for your business. Discover the growth, margin, and cash your generic planning tools can. Backed by Kleiner Perkins and Lightspeed.
About the role
You will work on problems that frontier models can't solve. Supply chain dynamics require modeling physical systems and processes. You will own the full research cycle, from hypothesis to production model, with direct visibility into real-world impact.
What they're looking for
- Strong foundations in machine learning — experience developing and evaluating forecasting models, and LLM pipelines
- Deep understanding of time-series forecasting, optimization, or related domains
- Experience working with messy, heterogeneous real-world data
- PhD or equivalent research experience preferred, but exceptional engineers with relevant industry experience will be considered
- Comfort operating in a fast-moving, early-stage environment where research directly feeds production systems
- Location: San Francisco (in-person, 5 days per week)
More about this role
Omnifold trains custom AI models for each customer's supply chain - purpose-built systems that forecast demand, optimize decisions, and adapt continuously to a changing world. The research team is responsible for the core intelligence that makes this possible: developing new model architectures, curating proprietary data assets, and pushing the boundaries of what ML can do.
You will work on problems that frontier models can't solve. Supply chain dynamics require modeling physical systems and processes.
You will own the full research cycle, from hypothesis to production model, with direct visibility into real-world impact.
You will work at the intersection of machine learning models, optimization, LLM reasoning capabilities, and proprietary data - a combination few research teams are building
Designing and training models for forecasting and optimization across complex, multi-variable supply chain environments
Building and curating proprietary data assets that carry signal about real-world physical and commercial systems
Integrating LLM knowledge and reasoning capabilities into purpose-built models to maximize accuracy and adaptability
Continuously improving model performance as...
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