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recommendation systems jobs in 2026 — demand, top roles hiring, and related skills

As of 2026-09-30, recommendation systems appears in 505 job postings indexed by Skillenai over the past 90 days — Machine Learning Engineer has the most postings mentioning recommendation systems, with demand share down 12.8% vs the prior 4 weeks.

Last updated · 90d ending 2026-09-30

Postings · last 90 days
505
Demand vs prior month
down 12.8% vs the prior 4 weeks
Top role · 19.0% of skill postings
Machine Learning Engineer
Top hiring metro
San Francisco

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Frequently asked questions about recommendation systems

+Is recommendation systems in demand in 2026?

Yes. recommendation systems appears in 505 job postings indexed by Skillenai over the 90 days ending 2026-09-30. Machine Learning Engineer accounts for the most postings mentioning recommendation systems (19.0% of all postings mentioning recommendation systems).

+What jobs require recommendation 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 recommendation systems are Machine Learning Researcher (18.4% of that role’s postings mention recommendation systems), Applied AI Scientist (10.3% of that role’s postings mention recommendation systems), Applied ML Engineer (9.5% of that role’s postings mention recommendation systems).

+What skills are commonly paired with recommendation systems?

Across job postings indexed by Skillenai (90 days ending 2026-09-30), recommendation systems most often appears alongside machine learning, Python, experimentation, A/B testing, personalization.

+Where is recommendation systems most in demand?

As of 2026-09-30, the metro areas posting the most jobs requiring recommendation systems are San Francisco, New York City, London, Bengaluru, San Jose, according to the Skillenai jobs index.

+How can I keep up with new recommendation systems content and jobs?

Skillenai indexes news, blog posts, and research papers mentioning recommendation systems alongside the jobs index. You can subscribe to a daily email digest of new recommendation systems content from your Skillenai account.

+Which skills come before and after recommendation 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 recommendation systems — last 90 days

Salary distribution

Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized

Career paths around recommendation systems

Skills documented before and after this skill across employer changes.

Historical career profiles · all locations

Skills before recommendation systems

Before recommendation systemspython → recommendation systems: 10 observed employer moves with this skill pairscikit-learn → recommendation systems: 4 observed employer moves with this skill pairsql → recommendation systems: 4 observed employer moves with this skill pairPower BI → recommendation systems: 3 observed employer moves with this skill pairAWS → recommendation systems: 3 observed employer moves with this skill pairtensorflow → recommendation systems: 3 observed employer moves with this skill pairpandas → recommendation systems: 3 observed employer moves with this skill pairLSTM → recommendation systems: 3 observed employer moves with this skill pairrecommendati…systemspython: 10 movespython10 movesscikit-learn: 4 movesscikit-learn4 movessql: 4 movessql4 movesPower BI: 3 movesPower BI3 movesAWS: 3 movesAWS3 movestensorflow: 3 movestensorflow3 movespandas: 3 movespandas3 movesLSTM: 3 movesLSTM3 moves

Skills after recommendation systems

After recommendation systemsrecommendation systems → pytorch: 5 observed employer moves with this skill pairrecommendation systems → tensorflow: 3 observed employer moves with this skill pairrecommendation systems → NLP: 3 observed employer moves with this skill pairrecommendation systems → python: 2 observed employer moves with this skill pairrecommendation systems → NLP models: 2 observed employer moves with this skill pairrecommendation systems → dashboards: 2 observed employer moves with this skill pairrecommendation systems → RAG pipelines: 2 observed employer moves with this skill pairrecommendation systems → Tableau: 2 observed employer moves with this skill pairrecommendati…systemspytorch: 5 movespytorch5 movestensorflow: 3 movestensorflow3 movesNLP: 3 movesNLP3 movespython: 2 movespython2 movesNLP models: 2 movesNLP models2 movesdashboards: 2 movesdashboards2 movesRAG pipelines: 2 movesRAG pipelines2 movesTableau: 2 movesTableau2 moves
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.

Observed connections and move counts
ConnectionMoves
Before: python10
Before: scikit-learn4
Before: sql4
Before: Power BI3
Before: AWS3
Before: tensorflow3
Before: pandas3
Before: LSTM3
After: pytorch5
After: tensorflow3
After: NLP3
After: python2
After: NLP models2
After: dashboards2
After: RAG pipelines2
After: Tableau2

Roles most likely to require recommendation systems

Among roles with at least 20 postings in the same period.

RolePostings mentioning skill% of role postings mentioning skill
Machine Learning Researcher718.4%
Applied AI Scientist310.3%
Applied ML Engineer29.5%
Applied Data Scientist49.3%
Machine Learning Manager28.0%
ML Engineering Manager27.7%
CTO24.5%
Delivery Manager14.5%
Field Engineer24.4%
React Native Engineer14.3%

Roles with the most recommendation systems postings

RolePostings mentioning skillShare of skill postings
Machine Learning Engineer9619.0%
Product Manager7114.1%
Data Scientist6913.7%
Software Engineer438.5%
ML Engineer183.6%
AI Engineer132.6%
Engineering Manager81.6%
Technical Program Manager81.6%
Machine Learning Researcher71.4%
AI/ML Engineer61.2%

Top companies posting jobs requiring recommendation systems

Employers ranked by indexed job postings in the last 90 days.

Top companies posting jobs requiring recommendation systems
CompanyPostings · 90 days
Spotify17
Grafanalabs14
Pinterest13
Reddit12
CLERA11
Coupang8
Jane Street7
Cloudbedsthirdpartyboard7
Adobe7
Elsevier6

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 recommendation systems

NamePostingsShare
San Francisco428.3%
New York City326.3%
London234.6%
Bengaluru163.2%
San Jose142.8%
Austin71.4%
Mountain View71.4%
Seattle71.4%
Brooklyn61.2%

Skills commonly paired with recommendation systems

machine learning (252)Python (212)experimentation (117)A/B testing (109)personalization (109)SQL (105)ranking (99)deep learning (92)PyTorch (88)LLMs (77)

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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 recommendation systems by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s recommendation systems postings by all recommendation 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: 0.3% 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 2,595 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
ad28684d3b45ce0d
data_as_of
2026-09-30
window_days
90
Hiring engineers who use recommendation systems?

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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Compiled by Jared Rand · Data sourced from the Skillenai labor market index