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Omnifold

AI-driven supply chain forecasting and optimization

Member of Technical Staff, Machine Learning Research Scientist

AI ResearchConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USA

About the Job

Requirements

  • Strong foundations in machine learning, including experience developing and evaluating forecasting models and large language model pipelines.
  • Deep understanding of time-series forecasting, optimization, or related domains.
  • Experience working with messy, heterogeneous real-world data.
  • Comfort operating in a fast-moving, early-stage environment where research directly feeds production systems.

Responsibilities

  • Design and train models for forecasting and optimization across complex, multi-variable supply chain environments.
  • Build and curate proprietary data assets that carry signal about real-world physical and commercial systems.
  • Integrate large language model knowledge and reasoning capabilities into purpose-built models to maximize accuracy and adaptability.
  • Continuously improve model performance as market conditions shift, including consumer sentiment, product launches, geopolitical changes, and competitive dynamics.

Desired Qualifications

  • A PhD or equivalent research experience is preferred; exceptional engineers with relevant industry experience will also be considered.

About the Company

Omnifold

AI-driven supply chain forecasting and optimization

Omnifold.ai uses artificial intelligence to help large and fast-growing brands and multinational companies manage supply chains and investment decisions. Its AI systems analyze data to predict demand and optimize plans for inventory, production, procurement, logistics, and marketing. The company relies on self-improving AI that learns from new data to improve forecasts and recommendations over time. By giving organizations better visibility and forward-looking guidance, Omnifold.ai aims to reduce waste, lower costs, and improve reliability in supply chains and investment outcomes. The goal is to help clients anticipate future needs, make smarter choices, and execute plans more effectively in a complex global market.

Company size
11-50
Company stage
Not listed
Total funding
Not listed
Headquarters
Not listed
Founded
2024

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