MTS - ML Research Engineer

Omnifold; San Francisco (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

Omnifold in San Francisco is seeking a Member of Technical Staff, ML Research Engineer, to work on developing custom AI models for supply chains. The role requires expertise in machine learning and aims to maximize operational efficiency in various dynamical environments.

The ideal candidate will have 5+ years in machine learning engineering, particularly in deep learning and time-series analysis. You will own the full cycle of research and directly see the impact your work has on real-world systems.

Qualifications

• 5+ years of industry machine learning engineering experience.
• Experience with time-series forecasting and related domains.
• Understanding of LLMs and practical system design.

Responsibilities

• Train models for forecasting across complex supply chain environments.
• Build and curate proprietary data assets.
• Integrate LLM capabilities into custom models.
• Continuously improve model performance based on market shifts.

Skills

Machine learning engineering

Deep learning models

Time-series forecasting

Mathematical modeling

Optimization

Large code bases

LLM understanding

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:

• 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.

What you'll own:

• 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 market conditions shift (consumer sentiment, product launches, geopolitical changes, competitive dynamics)

What we're looking for:

• 5+ years of industry machine learning engineering, including experimenting with deep learning models
• Experience with time-series forecasting, mathematical modeling, optimization, or related domains
• Understanding of LLMs, including fundamentals and practical system design including tool use and evaluation design
• Experience working with messy, heterogeneous real-world data
• Experience working with large code bases
• Academic or industry research experience preferred
• 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.