Join Omnifold as an ML Research Engineer to tackle complex supply chain challenges with innovative AI models.
Posted by employer 5 months ago
First seen on Joblaze 1 week ago
Last verified on the company career page 1 day ago
Skills & Technologies
AI in the day-to-day
Omnifold trains custom AI models for supply chain management, integrating LLM knowledge and reasoning capabilities.
Requirements
- Experience
- 5+ years
Not disclosed in this posting: compensation, visa sponsorship.
Joblaze summary
In this role, the ML Research Engineer at Omnifold focuses on developing and refining machine learning models tailored for complex supply chain challenges, ensuring they can adapt to real-world dynamics. Key skills include expertise in time-series forecasting, optimization, and a solid understanding of large language models, alongside experience with diverse data sets. This position is ideal for seasoned professionals with over five years in machine learning, particularly those who thrive in fast-paced, innovative environments where research directly influences production outcomes.
Joblaze insights
- Listed 9 days ago on Joblaze — tracked directly from Omnifold's career page.
- Omnifold has 11 other open roles including Head of Research, MTS - Infrastructure, Director of Applied AI.
- 798 active Machine Learning roles on Joblaze right now.
- 28 active Optimization roles on Joblaze right now.
- 6 active time-series forecasting roles on Joblaze right now.
- Typical Machine Learning salary we track runs $145,000–$230,000 (median $185,000, across 355 roles). See Machine Learning salary trends
Quick facts
- Is the MTS - ML Research Engineer role remote?
- No — this is an on-site role in San Francisco HQ.
- How much experience is required?
- At least 5 years of relevant experience for this MTS - ML Research Engineer role.
- Where is the role based?
- Omnifold is hiring for this position in San Francisco HQ.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: Machine Learning, Optimization, time-series forecasting.
- What seniority level is this role?
- Omnifold targets staff-level candidates for this position.
- Is this full-time or contract?
- Full-time for this MTS - ML Research Engineer role at Omnifold.
From the original posting
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 and experimentation
Experience with time-series forecasting, mathematical modeling, optimization, or related domains
Understanding of LLMs, including fundamentals and practical system design including tool use and eval 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.