MTS - ML Research Engineer
San Francisco, CAFull-time$95–320K/yrPosted 5mo agoStill listed 3 days ago
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Job overview
Omnifold is hiring a MTS - ML Research Engineer. Omnifold seeks a Member of Technical Staff who will advance its supply‑chain AI by developing novel model architectures, curating proprietary data, and integrating LLM reasoning. The role involves full‑cycle research from hypothesis through production, with direct impact on forecasting and optimization in dynamic, real‑world environments.
Key focus areas include Train models for forecasting and optimization across complex, multi-variable supply chain environments, Build and curate proprietary data assets that capture real‑world physical and commercial system signals, and Integrate LLM knowledge and reasoning capabilities into purpose‑built models to maximize accuracy and adaptability.
Successful candidates bring 5+ Years Industry Machine Learning Engineering. Important skills include Forecasting, Mathematical Modeling, Performance Optimization, LLMs, Practical System Design, and Tool Use. Preferred (not required): Academic Research Experience and Industry Research Experience.
Skills & qualifications
Skills
Qualifications
Full job description
Member of Technical Staff, ML Research Engineer
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.
What makes this job interesting:
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You will work on problems that frontier models can't solve. Supply chain dynamics require modeling physical systems and processes.
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You will own the full research cycle, from hypothesis to production model, with direct visibility into real-world impact.
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You will work at the intersection of machine learning models, optimization, LLM reasoning capabilities, and proprietary data - a combination few research teams are building
What you'll own:
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Training models for forecasting and optimization across complex, multi-variable supply chain environments
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Building and curating proprietary data assets that carry signal about real-world physical and commercial systems
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Integrating LLM knowledge and reasoning capabilities into purpose-built models to maximize accuracy and adaptability
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Continuously improving model performance as market conditions shift (consumer sentiment, product launches, geopolitical changes, competitive dynamics)
What we're looking for:
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5+ years of industry machine learning engineering, including and experimentation
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Experience with time-series forecasting, mathematical modeling, optimization, or related domains
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Understanding of LLMs, including fundamentals and practical system design including tool use and eval design
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Experience working with messy, heterogeneous real-world data
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Experience working with large code bases
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Academic or industry research experience preferred
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Comfort operating in a fast-moving, early-stage environment where research directly feeds production systems
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.
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