Active learning jobs in 2026 — demand, top roles hiring, and related skills

As of 2026-10-01, Active learning appears in 151 job postings indexed by Skillenai over the past 90 days — Machine Learning Engineer has the most postings mentioning Active learning, with demand share down 13.3% vs the prior 4 weeks.

Last updated · 90d ending 2026-10-01

Postings · last 90 days
151
Demand vs prior month
down 13.3% vs the prior 4 weeks
Top role · 14.6% of skill postings
Top hiring metro
San Francisco

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Frequently asked questions about Active learning

+Is Active learning in demand in 2026?

Yes. Active learning appears in 151 job postings indexed by Skillenai over the 90 days ending 2026-10-01. Machine Learning Engineer accounts for the most postings mentioning Active learning (14.6% of all postings mentioning Active learning).

+What jobs require Active learning?

According to the Skillenai jobs index over the 90 days ending 2026-10-01, among roles with at least 20 postings, the highest shares mentioning Active learning are ML Scientist (34.6% of that role’s postings mention Active learning), Postdoctoral Researcher (9.5% of that role’s postings mention Active learning), ML Infrastructure Engineer (4.2% of that role’s postings mention Active learning).

+What skills are commonly paired with Active learning?

Across job postings indexed by Skillenai (90 days ending 2026-10-01), Active learning most often appears alongside Python, PyTorch, machine learning, JAX, TensorFlow.

+Where is Active learning most in demand?

As of 2026-10-01, the metro areas posting the most jobs requiring Active learning are San Francisco, Sunnyvale, Mountain View, New York City, Toronto, according to the Skillenai jobs index.

+How can I keep up with new Active learning content and jobs?

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

+Which skills come before and after Active 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 Active learning — last 90 days

Salary distribution

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

Career paths around Active learning

Skills documented before and after this skill across employer changes.

Historical career profiles · all locations

Skills before Active learning

Before Active learningPostgreSQL → Active learning: 2 observed employer moves with this skill pairlabeled training data → Active learning: 1 observed employer moves with this skill pairsequential decision making → Active learning: 1 observed employer moves with this skill pair3D cardiac segmentation → Active learning: 1 observed employer moves with this skill pairrobustness → Active learning: 1 observed employer moves with this skill pairMYSQL → Active learning: 1 observed employer moves with this skill pairQuality Assurance → Active learning: 1 observed employer moves with this skill pairData Science → Active learning: 1 observed employer moves with this skill pairActivelearningPostgreSQL: 2 movesPostgreSQL2 moveslabeled training data: 1 moveslabeled trainingdata1 movessequential decision making: 1 movessequentialdecision making1 moves3D cardiac segmentation: 1 moves3D cardiacsegmentation1 movesrobustness: 1 movesrobustness1 movesMYSQL: 1 movesMYSQL1 movesQuality Assurance: 1 movesQuality Assurance1 movesData Science: 1 movesData Science1 moves

Skills after Active learning

After Active learningActive learning → Electron IPC: 1 observed employer moves with this skill pairActive learning → python: 1 observed employer moves with this skill pairActive learning → modular component-based architecture: 1 observed employer moves with this skill pairActive learning → Yahoo Search: 1 observed employer moves with this skill pairActive learning → CNN: 1 observed employer moves with this skill pairActive learning → sql: 1 observed employer moves with this skill pairActive learning → statistical NLP: 1 observed employer moves with this skill pairActive learning → information extraction: 1 observed employer moves with this skill pairActivelearningElectron IPC: 1 movesElectron IPC1 movespython: 1 movespython1 movesmodular component-based architecture: 1 movesmodularcomponent-basedarchitecture1 movesYahoo Search: 1 movesYahoo Search1 movesCNN: 1 movesCNN1 movessql: 1 movessql1 movesstatistical NLP: 1 movesstatistical NLP1 movesinformation extraction: 1 movesinformationextraction1 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: PostgreSQL2
Before: labeled training data1
Before: sequential decision making1
Before: 3D cardiac segmentation1
Before: robustness1
Before: MYSQL1
Before: Quality Assurance1
Before: Data Science1
After: Electron IPC1
After: python1
After: modular component-based architecture1
After: Yahoo Search1
After: CNN1
After: sql1
After: statistical NLP1
After: information extraction1

Roles most likely to require Active learning

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

RolePostings mentioning skill% of role postings mentioning skill
ML Scientist934.6%
Postdoctoral Researcher29.5%
ML Infrastructure Engineer24.2%
Adjunct Faculty14.2%
AI Research Scientist33.8%
ML Researcher13.0%
Algorithm Engineer12.9%
Machine Learning Research Engineer12.9%
Robotics Engineer22.9%
Machine Learning Researcher12.6%

Roles with the most Active learning postings

RolePostings mentioning skillShare of skill postings
Machine Learning Engineer2214.6%
Data Scientist159.9%
ML Engineer117.3%
Software Engineer117.3%
ML Scientist96.0%
Machine Learning Data Operations Manager42.6%
Research Engineer42.6%
AI Research Scientist32.0%
Engineering Manager32.0%
Machine Learning Scientist32.0%

Top companies posting jobs requiring Active learning

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

Top companies posting jobs requiring Active learning
CompanyPostings · 90 days
Waymo13
Lila Sciences11
Wayve8
Coalition4
Hadrian-automation4
May Mobility3
Applied Intuition3
Spotify3
CLERA3
Applied3

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 Active learning

NamePostingsShare
San Francisco1912.6%
Sunnyvale149.3%
Mountain View138.6%
New York City53.3%
Toronto53.3%
Los Angeles42.6%
Berlin32.0%
Chicago32.0%
Redwood City32.0%

Skills commonly paired with Active learning

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How this was computed

Counts derive from the Skillenai jobs index over the 90 days ending 2026-10-01. 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 Active learning by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s Active learning postings by all Active 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
33d45ca5db12c80a
data_as_of
2026-10-01
window_days
90
Hiring engineers who use Active learning?

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