embeddings jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-09-30, embeddings appears in 1,840 job postings indexed by Skillenai over the past 90 days — AI Engineer has the most postings mentioning embeddings, with demand share up 5.1% vs the prior 4 weeks.
Last updated · 90d ending 2026-09-30
Which roles want embeddings?
Upload your resume and Skillenai will show which roles your embeddings experience fits, which skills you already cover, and what is missing.
Prepare to discuss embeddings in your interview
We’re building mock interviews informed by job postings and career profiles, to help you explain how you’ve used embeddings.
Frequently asked questions about embeddings
+Is embeddings in demand in 2026?
Yes. embeddings appears in 1,840 job postings indexed by Skillenai over the 90 days ending 2026-09-30. AI Engineer accounts for the most postings mentioning embeddings (14.1% of all postings mentioning embeddings).
+What jobs require embeddings?
According to the Skillenai jobs index over the 90 days ending 2026-09-30, among roles with at least 20 postings, the highest shares mentioning embeddings are Agent Architect (57.1% of that role’s postings mention embeddings), GenAI Engineer (29.3% of that role’s postings mention embeddings), Software Development Test Engineer (20.0% of that role’s postings mention embeddings).
+What skills are commonly paired with embeddings?
Across job postings indexed by Skillenai (90 days ending 2026-09-30), embeddings most often appears alongside Python, vector databases, RAG, prompt engineering, LLMs.
+Where is embeddings most in demand?
As of 2026-09-30, the metro areas posting the most jobs requiring embeddings are San Francisco, New York City, Bengaluru, London, Hyderabad, according to the Skillenai jobs index.
+How can I keep up with new embeddings content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning embeddings alongside the jobs index. You can subscribe to a daily email digest of new embeddings content from your Skillenai account.
+Which skills come before and after embeddings?
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 embeddings — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around embeddings
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before embeddings
Skills after embeddings
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 | 4 |
| Before: APIs | 3 |
| Before: sql | 2 |
| Before: kubernetes | 2 |
| Before: Pinecone | 2 |
| Before: neural networks | 2 |
| Before: azure ml | 2 |
| Before: Dynamic Programming | 2 |
| After: MLflow | 2 |
| After: GPT4 | 1 |
| After: scikit-learn | 1 |
| After: data quality checks | 1 |
| After: advanced retrieval techniques | 1 |
| After: feature extraction | 1 |
| After: stable diffusion | 1 |
| After: ci/cd | 1 |
Roles most likely to require embeddings
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Agent Architect | 20 | 57.1% |
| GenAI Engineer | 12 | 29.3% |
| Software Development Test Engineer | 6 | 20.0% |
| Gen AI Engineer | 4 | 20.0% |
| AI Engineering Lead | 6 | 17.6% |
| Artificial Intelligence Engineer | 4 | 16.7% |
| AI Data Scientist | 3 | 15.0% |
| Generative AI Engineer | 8 | 14.8% |
| Applied ML Engineer | 3 | 14.3% |
| Founding AI Engineer | 3 | 14.3% |
Roles with the most embeddings postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| AI Engineer | 260 | 14.1% |
| Software Engineer | 249 | 13.5% |
| Data Scientist | 145 | 7.9% |
| Machine Learning Engineer | 133 | 7.2% |
| ML Engineer | 48 | 2.6% |
| Product Manager | 46 | 2.5% |
| Data Engineer | 45 | 2.4% |
| AI/ML Engineer | 38 | 2.1% |
| AI Architect | 21 | 1.1% |
| Backend Engineer | 21 | 1.1% |
Top companies posting jobs requiring embeddings
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| CLERA | 63 |
| Elsevier | 44 |
| Cisco | 23 |
| Parloa | 23 |
| Accenture | 22 |
| State Street | 21 |
| Capco | 21 |
| Blend360 | 17 |
| Scale AI | 16 |
| ServiceNow | 16 |
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 embeddings
| Name | Postings | Share |
|---|---|---|
| San Francisco | 88 | 4.8% |
| New York City | 79 | 4.3% |
| Bengaluru | 62 | 3.4% |
| London | 42 | 2.3% |
| Hyderabad | 40 | 2.2% |
| Toronto | 30 | 1.6% |
| Berlin | 27 | 1.5% |
| Munich | 24 | 1.3% |
| Pune | 21 | 1.1% |
Skills commonly paired with embeddings
Get a daily email digest of new embeddings content
Skillenai indexes news articles, blog posts, and research papers that mention embeddings. Click below and we'll open a pre-filled daily digest — change the cadence to hourly or weekly if you prefer, then save. Free account required (~30 seconds).
Explore related pages
How this was computed
Counts derive from the Skillenai jobs index over the 90 days ending 2026-09-30. 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 embeddings by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s embeddings postings by all embeddings 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: 0.7% to 0.8%. 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 2,596 identified employers and covers 68% of earlier and 72% of latest indexed postings. Windows: 2026-06-02 to 2026-08-31 and 2026-06-30 to 2026-09-28 (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
- bd627d16a70bd1ff
- data_as_of
- 2026-09-30
- 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.
Skillenai for recruiters →