information retrieval jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-09-30, information retrieval appears in 560 job postings indexed by Skillenai over the past 90 days — Software Engineer has the most postings mentioning information retrieval.
Last updated · 90d ending 2026-09-30
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Frequently asked questions about information retrieval
+Is information retrieval in demand in 2026?
Yes. information retrieval appears in 560 job postings indexed by Skillenai over the 90 days ending 2026-09-30. Software Engineer accounts for the most postings mentioning information retrieval (40.0% of all postings mentioning information retrieval).
+What jobs require information retrieval?
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 information retrieval are Applied Research Engineer (11.5% of that role’s postings mention information retrieval), Applied ML Engineer (9.5% of that role’s postings mention information retrieval), Machine Learning Engineering Manager (5.1% of that role’s postings mention information retrieval).
+What skills are commonly paired with information retrieval?
Across job postings indexed by Skillenai (90 days ending 2026-09-30), information retrieval most often appears alongside Natural language processing, Python, networking, Distributed computing, large-scale system design.
+Where is information retrieval most in demand?
As of 2026-09-30, the metro areas posting the most jobs requiring information retrieval are Mountain View, Warsaw, New York City, San Francisco, San Jose, according to the Skillenai jobs index.
+How can I keep up with new information retrieval content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning information retrieval alongside the jobs index. You can subscribe to a daily email digest of new information retrieval content from your Skillenai account.
+Which skills come before and after information retrieval?
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 information retrieval — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around information retrieval
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before information retrieval
Skills after information retrieval
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: churning customers | 1 |
| Before: visualizations | 1 |
| Before: cosine similarity | 1 |
| Before: python | 1 |
| Before: machine learning algorithms | 1 |
| Before: Grader in signal and system course | 1 |
| Before: GDPR | 1 |
| Before: OSINT | 1 |
| After: spark | 1 |
| After: stateful multi-process execution | 1 |
| After: supervised learning | 1 |
| After: autonomous-driving | 1 |
| After: user experience | 1 |
| After: sql | 1 |
| After: Powerpoint | 1 |
| After: model evaluation | 1 |
Roles most likely to require information retrieval
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Applied Research Engineer | 3 | 11.5% |
| Applied ML Engineer | 2 | 9.5% |
| Machine Learning Engineering Manager | 3 | 5.1% |
| Software Engineering Manager | 36 | 5.0% |
| Principal Data Scientist | 2 | 5.0% |
| Applied Data Scientist | 2 | 4.7% |
| Applied Scientist | 11 | 4.4% |
| Research Software Engineer | 1 | 4.2% |
| Machine Learning Manager | 1 | 4.0% |
| Applied Research Scientist | 1 | 3.8% |
Roles with the most information retrieval postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Software Engineer | 224 | 40.0% |
| Machine Learning Engineer | 48 | 8.6% |
| Data Scientist | 36 | 6.4% |
| Software Engineering Manager | 36 | 6.4% |
| Product Manager | 21 | 3.8% |
| AI Engineer | 20 | 3.6% |
| Applied Scientist | 11 | 2.0% |
| ML Engineer | 11 | 2.0% |
| AI Software Engineer | 9 | 1.6% |
| Data Science Manager | 5 | 0.9% |
Top companies posting jobs requiring information retrieval
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| 216 | |
| Elsevier | 22 |
| 10 | |
| ServiceNow | 9 |
| YouTube | 8 |
| Nebius | 8 |
| Qualtrics | 8 |
| Workday | 7 |
| Coupang | 7 |
| Doctolib | 6 |
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 information retrieval
| Name | Postings | Share |
|---|---|---|
| Mountain View | 23 | 4.1% |
| Warsaw | 23 | 4.1% |
| New York City | 21 | 3.8% |
| San Francisco | 19 | 3.4% |
| San Jose | 11 | 2.0% |
| London | 10 | 1.8% |
| Seattle | 10 | 1.8% |
| Paris | 9 | 1.6% |
| Toronto | 9 | 1.6% |
Skills commonly paired with information retrieval
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Explore related pages
- Applied Research Engineer
- Applied ML Engineer
- Machine Learning Engineering Manager
- Software Engineering Manager
- Principal Data Scientist
- Applied Data Scientist
- Applied Scientist
- Research Software Engineer
- Natural language processing
- Python
- networking
- Distributed computing
- large-scale system design
- security
- data storage
- machine learning
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 information retrieval by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s information retrieval postings by all information retrieval 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). An adjusted trend is not shown because comparable posting coverage is insufficient.
- source
- Skillenai jobs index, deduplicated daily
- entity_id
- a37c1b1fb70b5e8f
- 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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