Full-Time
Infrastructure MTS
Updated on 8/1/2026
Omnifold
11-50 employees
AI-driven supply chain forecasting and optimization
No salary listed
San Francisco, CA, USA
In Person
Five days on-site per week in San Francisco.
 DevOps & Infrastructure (1) |
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- Experience with cloud computing, especially GPU workloads, and continuous integration/continuous delivery infrastructure-as-code.
- Familiarity with or interest in machine-learning workflows.
- Knowledge of security fundamentals, including encryption, access controls, and compliance basics.
- Proficiency in Python.
- A strong Computer Science background.
- Build reliable processes for deploying models and services into production.
- Manage data isolation between customers, product security, infrastructure hardening, and SOC 2 compliance.
- Manage cloud resources, including GPU allocation, instance sizing, and cost optimization.
- Build monitoring and logging to provide visibility into running and failing systems and their causes.
- Develop extract, transform, load pipelines from varied customer data sources and manage model versioning and lifecycles.
- Build automated test infrastructure to support reliable software releases.
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
N/A
Total Funding
N/A
Headquarters
N/A
Founded
2024
Get referred to Omnifold
See people who can refer or advise you
Simplify's Take
What believers are saying
- Strong fit for CPG brands facing new-SKU and launch forecasting.
- Multi-signal forecasting can improve inventory, procurement, logistics, and marketing decisions.
- Enterprise clients can fund expansion through partnerships and service revenue.
What critics are saying
- Lokad calls Omnifold weakly evidenced, hurting enterprise credibility.
- Complex data integration across ERP, CRM, and external sources increases deployment risk.
- A public forecast miss on inventory or service levels would damage trust quickly.
What makes Omnifold unique
- Purpose-built AI models each customer’s unique supply chain.
- Integrates internal data with external signals like geopolitics and sentiment.
- Focuses on forecasting, not generic LLM workflows.
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Benefits
Company Equity
Growth & Insights and Company News
6 month growth
↓ -17%1 year growth
↓ -17%2 year growth
↓ -17%What Not Your Mother's haircare is prioritizing in 2026. In a recent CGT webinar Eric Falkenmayer, Senior Manager of Demand Planning at Not Your Mother's Hair Care, shared how critical trends in AI are playing out inside a fast-growing CPG brand, and where he sees the biggest opportunities ahead. CGT's latest supply chain report paints a clear picture: consumer goods manufacturers are moving from reactive to proactive stand regarding AI, and AI-powered demand forecasting is at the center of that shift. With 53% of companies planning major AI and machine learning upgrades in 2026, the question is no longer whether to adopt, it's how to apply these tools quickly where they'll drive the most value. From Reactive to Proactive When asked about the single most critical priority for supply chain leaders heading into 2026, Eric was direct: it's the shift from reacting to disruptions to making data-driven decisions ahead of them. That means connecting internal data like shipments, POS, CRM and inventory positioning with external signals and using purpose built AI to plan at a speed and scale. The "Holy Grail" of Demand Forecasting For Eric, the highest-value application of AI isn't strictly incremental accuracy gains on the base business. It's forecasting for innovation: new SKU launches, new customer expansions, and new channels where historical comps are limited. He called it "the holy grail of demand forecasting" and pointed to a recent pilot with Omnifold where forecasts based on their purpose built AI tracked significantly closer to actual shipments than the team's original projections using the same underlying data. Purpose-Built Over General-Purpose A recurring theme was the importance of choosing AI tools designed for the specific problem at hand. Eric drew a sharp distinction: a large language model like ChatGPT is built for conversation, and autonomous driving AI is built to navigate roads safely: you wouldn't swap them. Supply chain forecasting demands the same purpose built approach. Alignment between a tool's design and the problem it solves was a key factor in Not Your Mother's decision to partner with Omnifold. Watch the Full Conversation To hear Eric's full perspective including a detailed walkthrough of Demert's IBP process and a discussion on AI adoption gaps and sustainability visit and subscribe to the Omnifold YouTube channel.