foundation models jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-10-14, foundation models appears in 666 job postings indexed by Skillenai over the past 90 days — Machine Learning Engineer has the most postings mentioning foundation models, with demand share up 6.3% vs the prior 4 weeks.
Last updated · 90d ending 2026-10-14
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Frequently asked questions about foundation models
+Is foundation models in demand in 2026?
Yes. foundation models appears in 666 job postings indexed by Skillenai over the 90 days ending 2026-10-14. Machine Learning Engineer accounts for the most postings mentioning foundation models (9.3% of all postings mentioning foundation models).
+What jobs require foundation models?
According to the Skillenai jobs index over the 90 days ending 2026-10-14, among roles with at least 20 postings, the highest shares mentioning foundation models are Machine Learning Infrastructure Engineer (24.1% of that role’s postings mention foundation models), Deep Learning Engineer (16.1% of that role’s postings mention foundation models), ML Researcher (14.3% of that role’s postings mention foundation models).
+What skills are commonly paired with foundation models?
Across job postings indexed by Skillenai (90 days ending 2026-10-14), foundation models most often appears alongside machine learning, Python, PyTorch, Generative AI, fine-tuning.
+Where is foundation models most in demand?
As of 2026-10-14, the metro areas posting the most jobs requiring foundation models are San Francisco, Sunnyvale, Mountain View, New York City, London, according to the Skillenai jobs index.
+How can I keep up with new foundation models content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning foundation models alongside the jobs index. You can subscribe to a daily email digest of new foundation models content from your Skillenai account.
+Which skills come before and after foundation models?
The skill-flow chart shows skills documented in adjacent positions across observed employer changes. An outgoing skill is documented in the following position but not the preceding one. These are ideas to explore, not proven prerequisites, acquisition dates, or levels of mastery. Each ribbon counts employer moves with that skill pair; one move can contribute several pairs.
Weekly indexed postings requiring foundation models — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around foundation models
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before foundation models
Skills after foundation models
No published outgoing skill pairs yet.
How to read this chart · view counts
Each side is an independent set of observed employer moves, not the same people followed through three stages. Ribbon widths compare move counts within that side. Internal moves are not included.
The following position documents a skill that the preceding position does not. Skills must be linked to both positions, with clear dates and no overlap. One move can connect several skill pairs. These patterns suggest skills to explore; they do not establish prerequisites, when a skill was learned, or a higher skill level.
Source: Skillenai talent graph, historical career profiles. Historical descriptions and coverage can change. Only the leading published connections are shown.
| Connection | Moves |
|---|---|
| Before: python | 2 |
| Before: EEG | 1 |
| Before: docker | 1 |
| Before: Sensor acquisition | 1 |
| Before: computer vision | 1 |
| Before: 3D modelling | 1 |
| Before: GitLab | 1 |
| Before: Linux-based software | 1 |
Roles most likely to require foundation models
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Machine Learning Infrastructure Engineer | 7 | 24.1% |
| Deep Learning Engineer | 5 | 16.1% |
| ML Researcher | 4 | 14.3% |
| Machine Learning Research Scientist | 3 | 14.3% |
| AI Scientist | 11 | 13.8% |
| AI Research Scientist | 10 | 13.3% |
| Applied Researcher | 6 | 12.5% |
| Research Intern | 7 | 10.6% |
| AI/ML Scientist | 2 | 9.5% |
| Forward Deployed AI Engineer | 9 | 9.5% |
Roles with the most foundation models postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Machine Learning Engineer | 62 | 9.3% |
| AI Engineer | 42 | 6.3% |
| Research Scientist | 35 | 5.3% |
| ML Engineer | 33 | 5.0% |
| Data Scientist | 30 | 4.5% |
| Software Engineer | 28 | 4.2% |
| Product Manager | 27 | 4.1% |
| AI/ML Engineer | 13 | 2.0% |
| AI Scientist | 11 | 1.7% |
| AI Research Scientist | 10 | 1.5% |
Top companies posting jobs requiring foundation models
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Waymo | 29 |
| Wayve | 17 |
| AstraZeneca | 14 |
| Cloudera | 13 |
| Verta | 11 |
| Capco | 11 |
| Novartis | 10 |
| Amazon Web Services (AWS) | 10 |
| Bosch | 9 |
| ServiceNow | 9 |
Job postings indexed over the past 90 days, grouped by resolved employer. Counts are postings, not hires. Companies without a published page appear without a link.
Top metros hiring for foundation models
| Name | Postings | Share |
|---|---|---|
| San Francisco | 42 | 6.3% |
| Sunnyvale | 36 | 5.4% |
| Mountain View | 34 | 5.1% |
| New York City | 24 | 3.6% |
| London | 15 | 2.3% |
| Boston | 14 | 2.1% |
| Amsterdam | 13 | 2.0% |
| Cambridge | 13 | 2.0% |
| Berlin | 12 | 1.8% |
Skills commonly paired with foundation models
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How this was computed
Counts derive from the Skillenai jobs index over the 90 days ending 2026-10-14. Skills are resolved against the Skillenai canonical taxonomy, so the same entity is counted whether a posting writes 'Python', 'Python 3', or 'python'. Role prevalence divides postings mentioning foundation models by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s foundation models postings by all foundation models postings, including postings without a role. Shares need not sum to 100% for the displayed roles. Pages refresh weekly (or daily for the top-50 most-requested skills). Adjusted posting share: 0.3% to 0.3%. Demand share change is the relative percentage change between these adjusted shares. Each employer-and-ATS group has at least 10 postings in each 90-day window; its earlier posting count supplies the same weight in both windows. The panel includes 2,614 identified employers and covers 71% of earlier and 72% of latest indexed postings. Windows: 2026-06-16 to 2026-09-14 and 2026-07-14 to 2026-10-12 (UTC; end dates excluded). The windows overlap by 62 days. Dates reflect indexing, not the employer’s posting date. This measures posting mix, not total hiring or market-wide demand. Matching excludes entrants and exits; changes in crawl completeness within an employer or ATS can still affect the result.
- source
- Skillenai jobs index, deduplicated daily
- entity_id
- 8d6f5aea4009fcb3
- data_as_of
- 2026-10-14
- window_days
- 90
The demand, skills, and geo numbers on this page come from the same Skillenai labor market index that powers our API. Use it for compensation benchmarking, hiring-competition analysis, and skill-adoption tracking.
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