Recommender systems jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-09-30, Recommender systems appears in 171 job postings indexed by Skillenai over the past 90 days — Machine Learning Engineer has the most postings mentioning Recommender systems, with demand share down 14.5% vs the prior 4 weeks.
Last updated · 90d ending 2026-09-30
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Frequently asked questions about Recommender systems
+Is Recommender systems in demand in 2026?
Yes. Recommender systems appears in 171 job postings indexed by Skillenai over the 90 days ending 2026-09-30. Machine Learning Engineer accounts for the most postings mentioning Recommender systems (21.6% of all postings mentioning Recommender systems).
+What jobs require Recommender systems?
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 Recommender systems are Machine Learning Manager (28.0% of that role’s postings mention Recommender systems), Applied Researcher (14.3% of that role’s postings mention Recommender systems), Applied Scientist (2.4% of that role’s postings mention Recommender systems).
+What skills are commonly paired with Recommender systems?
Across job postings indexed by Skillenai (90 days ending 2026-09-30), Recommender systems most often appears alongside machine learning, Python, PyTorch, deep learning, SQL.
+Where is Recommender systems most in demand?
As of 2026-09-30, the metro areas posting the most jobs requiring Recommender systems are San Francisco, Toronto, Mountain View, New York City, London, according to the Skillenai jobs index.
+How can I keep up with new Recommender systems content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning Recommender systems alongside the jobs index. You can subscribe to a daily email digest of new Recommender systems content from your Skillenai account.
+Which skills come before and after Recommender systems?
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 Recommender systems — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around Recommender systems
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before Recommender systems
Skills after Recommender systems
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: spark | 1 |
| Before: NLP | 1 |
| Before: Lie algebras | 1 |
| Before: ETL/ELT | 1 |
| Before: predictive analytics | 1 |
| Before: Hive UDF | 1 |
| Before: Just Train Twice (JTT) | 1 |
| After: .NET | 2 |
| After: C# | 2 |
| After: sql | 2 |
| After: feature extraction | 1 |
| After: machine learning algorithms | 1 |
| After: Agentic RAG | 1 |
| After: vertex ai | 1 |
| After: exploratory data analysis (EDA) | 1 |
Roles most likely to require Recommender systems
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Machine Learning Manager | 7 | 28.0% |
| Applied Researcher | 6 | 14.3% |
| Applied Scientist | 6 | 2.4% |
| Data Science Director | 1 | 1.7% |
| Machine Learning Engineering Manager | 1 | 1.7% |
| Machine Learning Engineer | 37 | 1.6% |
| AI Agent Engineer | 1 | 1.4% |
| Data Science Manager | 3 | 1.3% |
| Data Scientist Intern | 1 | 1.1% |
| AI Engineering Manager | 1 | 1.1% |
Roles with the most Recommender systems postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Machine Learning Engineer | 37 | 21.6% |
| Data Scientist | 36 | 21.1% |
| AI Engineer | 9 | 5.3% |
| Engineering Manager | 7 | 4.1% |
| Machine Learning Manager | 7 | 4.1% |
| Product Manager | 7 | 4.1% |
| Applied AI/ML Scientist | 6 | 3.5% |
| Applied Researcher | 6 | 3.5% |
| Applied Scientist | 6 | 3.5% |
| Data Science Manager | 3 | 1.8% |
Top companies posting jobs requiring Recommender systems
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| 13 | |
| 13 | |
| eBay | 12 |
| 11 | |
| Faire | 6 |
| Baytech Consulting | 4 |
| Catawiki | 4 |
| Spotify | 3 |
| Tencent | 3 |
| Quora | 3 |
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 Recommender systems
| Name | Postings | Share |
|---|---|---|
| San Francisco | 17 | 9.9% |
| Toronto | 13 | 7.6% |
| Mountain View | 12 | 7.0% |
| New York City | 9 | 5.3% |
| London | 7 | 4.1% |
| Amsterdam | 5 | 2.9% |
| Bengaluru | 4 | 2.3% |
| Denver | 4 | 2.3% |
| Palo Alto | 4 | 2.3% |
Skills commonly paired with Recommender systems
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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 Recommender systems by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s Recommender systems postings by all Recommender systems 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
- 7292940e6df817f9
- 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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