Reinforcement learning jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-11-04, Reinforcement learning appears in 2,051 job postings indexed by Skillenai over the past 90 days — Machine Learning Engineer has the most postings mentioning Reinforcement learning, with demand share up 11.1% vs the prior 4 weeks.
Last updated · 90d ending 2026-11-04
Which roles want Reinforcement learning?
Upload your resume and Skillenai will show which roles your Reinforcement learning experience fits, which skills you already cover, and what is missing.
Frequently asked questions about Reinforcement learning
+Is Reinforcement learning in demand in 2026?
Yes. Reinforcement learning appears in 2,051 job postings indexed by Skillenai over the 90 days ending 2026-11-04. Machine Learning Engineer accounts for the most postings mentioning Reinforcement learning (10.1% of all postings mentioning Reinforcement learning).
+What jobs require Reinforcement learning?
According to the Skillenai jobs index over the 90 days ending 2026-11-04, among roles with at least 20 postings, the highest shares mentioning Reinforcement learning are Member of Technical Staff (29.3% of that role’s postings mention Reinforcement learning), Research Engineer (26.7% of that role’s postings mention Reinforcement learning), AI Researcher (26.1% of that role’s postings mention Reinforcement learning).
+What skills are commonly paired with Reinforcement learning?
Across job postings indexed by Skillenai (90 days ending 2026-11-04), Reinforcement learning most often appears alongside Python, PyTorch, Machine Learning, deep learning, C++.
+Where is Reinforcement learning most in demand?
As of 2026-11-04, the metro areas posting the most jobs requiring Reinforcement learning are San Francisco, New York City, London, Sunnyvale, Mountain View, according to the Skillenai jobs index.
+How can I keep up with new Reinforcement learning content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning Reinforcement learning alongside the jobs index. You can subscribe to a daily email digest of new Reinforcement learning content from your Skillenai account.
+Which skills come before and after Reinforcement 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 Reinforcement learning — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around Reinforcement learning
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before Reinforcement learning
Skills after Reinforcement 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: python | 18 |
| Before: NLP | 7 |
| Before: docker | 7 |
| Before: tensorflow | 7 |
| Before: machine learning | 7 |
| Before: spark | 6 |
| Before: scikit-learn | 5 |
| Before: Power BI | 5 |
| After: rag | 5 |
| After: python | 5 |
| After: LLMs | 4 |
| After: langchain | 4 |
| After: docker | 4 |
| After: prompt engineering | 4 |
| After: Kafka | 3 |
| After: large language models | 2 |
Roles most likely to require Reinforcement learning
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Member of Technical Staff | 12 | 29.3% |
| Research Engineer | 181 | 26.7% |
| AI Researcher | 31 | 26.1% |
| Research Intern | 19 | 24.4% |
| Lecturer | 7 | 24.1% |
| Machine Learning Researcher | 8 | 22.9% |
| AI Research Scientist | 18 | 22.8% |
| Robotics Engineer | 21 | 22.3% |
| Autonomy Engineer | 8 | 22.2% |
| Postdoctoral Researcher | 5 | 21.7% |
Roles with the most Reinforcement learning postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Machine Learning Engineer | 208 | 10.1% |
| Research Scientist | 205 | 10.0% |
| Software Engineer | 189 | 9.2% |
| Research Engineer | 181 | 8.8% |
| Data Scientist | 101 | 4.9% |
| ML Engineer | 98 | 4.8% |
| AI Engineer | 86 | 4.2% |
| Applied Scientist | 38 | 1.9% |
| AI/ML Engineer | 32 | 1.6% |
| AI Researcher | 31 | 1.5% |
Top companies posting jobs requiring Reinforcement learning
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| CLERA | 120 |
| NVIDIA | 64 |
| Waymo | 63 |
| Scale AI | 47 |
| Anthropic | 45 |
| General Motors | 39 |
| Wayve | 36 |
| Helsing | 36 |
| 33 | |
| Bosch | 25 |
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 Reinforcement learning
| Name | Postings | Share |
|---|---|---|
| San Francisco | 289 | 14.1% |
| New York City | 127 | 6.2% |
| London | 80 | 3.9% |
| Sunnyvale | 76 | 3.7% |
| Mountain View | 68 | 3.3% |
| Singapore | 57 | 2.8% |
| Santa Clara | 44 | 2.1% |
| Munich | 42 | 2.0% |
| Palo Alto | 40 | 2.0% |
Skills commonly paired with Reinforcement learning
Explore related pages
How this was computed
Weekly job-count charts omit the incomplete current week (weeks start Monday, UTC). Totals include all postings in the stated window. Counts derive from the Skillenai jobs index over the 90 days ending 2026-11-04. 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 Reinforcement learning by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s Reinforcement learning postings by all Reinforcement 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: 0.8% to 0.9%. 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 2,472 identified employers and covers 71% of earlier and 70% of latest indexed postings. Windows: 2026-07-07 to 2026-10-05 and 2026-08-04 to 2026-11-02 (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
- df034df3eb79e2e3
- data_as_of
- 2026-11-04
- window_days
- 90