representation learning jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-09-30, representation learning appears in 210 job postings indexed by Skillenai over the past 90 days — Machine Learning Engineer has the most postings mentioning representation learning, with demand share down 3.2% vs the prior 4 weeks.
Last updated · 90d ending 2026-09-30
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Frequently asked questions about representation learning
+Is representation learning in demand in 2026?
Yes. representation learning appears in 210 job postings indexed by Skillenai over the 90 days ending 2026-09-30. Machine Learning Engineer accounts for the most postings mentioning representation learning (14.8% of all postings mentioning representation learning).
+What jobs require representation learning?
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 representation learning are Machine Learning Scientist (17.1% of that role’s postings mention representation learning), ML Researcher (9.1% of that role’s postings mention representation learning), AI Research Scientist (8.6% of that role’s postings mention representation learning).
+What skills are commonly paired with representation learning?
Across job postings indexed by Skillenai (90 days ending 2026-09-30), representation learning most often appears alongside machine learning, PyTorch, Python, deep learning, TensorFlow.
+Where is representation learning most in demand?
As of 2026-09-30, the metro areas posting the most jobs requiring representation learning are San Francisco, Boston, New York City, San Jose, Berlin, according to the Skillenai jobs index.
+How can I keep up with new representation learning content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning representation learning alongside the jobs index. You can subscribe to a daily email digest of new representation learning content from your Skillenai account.
+Which skills come before and after representation learning?
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 representation learning — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around representation learning
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before representation learning
Skills after representation learning
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: Azure DevOps | 1 |
| Before: python | 1 |
| Before: Apache Kafka | 1 |
| Before: docker | 1 |
| Before: sql | 1 |
| Before: transformer-like architectures | 1 |
| Before: MLflow | 1 |
| Before: PySpark | 1 |
| After: MoCo-style contrastive learning | 1 |
| After: CommonCrawlTables corpus | 1 |
| After: pre-train | 1 |
| After: LLM based unsupervised reranker | 1 |
| After: Multiple modalities | 1 |
| After: table retrieval model (TAPAS) | 1 |
| After: WikiTables | 1 |
| After: QA component | 1 |
Roles most likely to require representation learning
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Machine Learning Scientist | 24 | 17.1% |
| ML Researcher | 3 | 9.1% |
| AI Research Scientist | 7 | 8.6% |
| AI Researcher | 9 | 7.6% |
| Principal Data Scientist | 3 | 7.5% |
| Machine Learning Researcher | 2 | 5.3% |
| Applied Researcher | 2 | 4.8% |
| AI Security Researcher | 1 | 4.5% |
| ML Scientist | 1 | 3.8% |
| AI Scientist | 3 | 3.4% |
Roles with the most representation learning postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Machine Learning Engineer | 31 | 14.8% |
| Machine Learning Scientist | 24 | 11.4% |
| Data Scientist | 22 | 10.5% |
| Research Scientist | 20 | 9.5% |
| AI Researcher | 9 | 4.3% |
| Applied Scientist | 8 | 3.8% |
| AI Research Scientist | 7 | 3.3% |
| AI/ML Researcher | 7 | 3.3% |
| Head of AI Research | 6 | 2.9% |
| AI Engineer | 5 | 2.4% |
Top companies posting jobs requiring representation learning
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Wayve | 9 |
| Whoop | 8 |
| Cisco | 7 |
| Adyen | 5 |
| The New York Times | 5 |
| CLERA | 5 |
| 5 | |
| Adobe | 5 |
| 5 | |
| Capital One | 4 |
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 representation learning
| Name | Postings | Share |
|---|---|---|
| San Francisco | 12 | 5.7% |
| Boston | 9 | 4.3% |
| New York City | 7 | 3.3% |
| San Jose | 7 | 3.3% |
| Berlin | 6 | 2.9% |
| London | 6 | 2.9% |
| Sunnyvale | 6 | 2.9% |
| Toronto | 6 | 2.9% |
| Amsterdam | 5 | 2.4% |
Skills commonly paired with representation learning
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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 representation learning by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s representation learning postings by all representation learning 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: 0.1% to 0.1%. 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 2,595 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
- 5a7341ac2478e035
- 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.
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