Applied AI
Company websiteomnifold.ai (Omnifold, opens in new tab)
Base salary $150K to $300K
Employer-published annual base salary, USD. Advertised base range in San Francisco, CA
You apply on the employer's own site. Six Figure Work does not handle applications.
- LocationSan Francisco, CA
- WorkplaceOn-site
- Status at last checkSource accepting applications when checked
- Last checked (2 h ago) · observed since
About this job
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.
What You’ll Do
Engage directly with real customer problems to deeply understand data and operational challenges
Frame customer problems clearly
Lead technical and value-oriented demos that resonate with both technical and non-technical stakeholders
Work hands-on with data using Python and SQL
Build custom demos and prototypes to support GTM activities
Act as a trusted technical partner during evaluations, pilots, and implementations
Support active customer deployments to ensure full value of platform is realized
What We’re Looking For
2+ years of experience as a Data Scientist or Applied AI Scientist
Prior experience as a data scientist or selling a product which required a foundational understanding of machine learning
Excellent project management skills and attention to detail
Experience using technologies such as Python and SQL to answer complex questions and solve business problems
Strong business intuition paired with technical rigor
Hands-on, builder mindset; willingness to dive into new technologies / topics as the situation requires
Confident engaging with business and technical stakeholders alike
How We Assess This Role
Technical assessment: Core data analysis and machine learning skills
Business impact presentation: In-depth walkthrough of an example where you have solved a business problem using AI, machine learning, or optimization
Demo: Create a demo in our application and showcase it in front of our team
Compensation: $150K to $300K base | Equity | Health, dental, vision | 401(k)
Source: Omnifold on Ashby · View original posting (opens in new tab)
Pay in context
This role's base floor ($150,000) is above 17% of the 1,506 Data & ML roles in California listed on this site; 58 others list the same floor. Their median floor is $188,248; the middle half runs from $159,256 to $229,800.
Only jobs on this site count, and each one publishes a base salary of $100,000 or more, so this compares six-figure jobs with each other, not with the whole market. Computed Nov 7, 2026.
This listingApplied AI$150K to $300K
MTS - Infrastructure$150K to $300K
MTS - ML Research Scientist$150K to $300K
MTS - Applications$150K to $300K
Director of Applied AI$200K to $300K
Chart as a table
| Role | Published base |
|---|---|
| Applied AI (this listing) | $150K - $300K |
| MTS - Infrastructure | $150K - $300K |
| MTS - ML Research Scientist | $150K - $300K |
| MTS - Applications | $150K - $300K |
| Director of Applied AI | $200K - $300K |
Observed changes
- First observed on the employer's board
- Began meeting the published-salary criteria
- Observed since
- Employer-stated posting date
- 2026-07-07
Where this pay comes from
Location as the employer wrote it: San Francisco HQ
Compensation: $150K to $300K base | Equity | Health, dental, vision | 401(k)
- Method
- Employer text, read with fixed rules and re-verified
- Checked
- Rule set
- us-base-v2
The pay passage appears in the employer's own job text. Fixed, deterministic rules located it, and the passage below was checked against the stored copy of the posting.
What we verified
- The pay figures come from the employer's own listing, quoted exactly above.
- The pay is an annual base salary in US dollars with a minimum of at least $100,000.
- The role was publicly listed on the employer's job board when last checked.
We do not verify what the employer will actually offer, whether the employer is still recruiting for the role, or anything beyond what the employer published. Full methodology.