ML infrastructure jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-10-29, ML infrastructure appears in 165 job postings indexed by Skillenai over the past 90 days — Software Engineer has the most postings mentioning ML infrastructure, with demand share down 17.2% vs the prior 4 weeks.
Last updated · 90d ending 2026-10-29
Which roles want ML infrastructure?
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Frequently asked questions about ML infrastructure
+Is ML infrastructure in demand in 2026?
Yes. ML infrastructure appears in 165 job postings indexed by Skillenai over the 90 days ending 2026-10-29. Software Engineer accounts for the most postings mentioning ML infrastructure (34.5% of all postings mentioning ML infrastructure).
+What jobs require ML infrastructure?
According to the Skillenai jobs index over the 90 days ending 2026-10-29, among roles with at least 20 postings, the highest shares mentioning ML infrastructure are Tech Lead Manager (3.8% of that role’s postings mention ML infrastructure), ML Ops Engineer (2.7% of that role’s postings mention ML infrastructure), ML Research Engineer (2.6% of that role’s postings mention ML infrastructure).
+What skills are commonly paired with ML infrastructure?
Across job postings indexed by Skillenai (90 days ending 2026-10-29), ML infrastructure most often appears alongside Python, Distributed systems, machine learning, data pipelines, model evaluation.
+Where is ML infrastructure most in demand?
As of 2026-10-29, the metro areas posting the most jobs requiring ML infrastructure are San Francisco, London, Munich, New York City, Paris, according to the Skillenai jobs index.
+How can I keep up with new ML infrastructure content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning ML infrastructure alongside the jobs index. You can subscribe to a daily email digest of new ML infrastructure content from your Skillenai account.
+Which skills come before and after ML infrastructure?
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 ML infrastructure — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around ML infrastructure
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before ML infrastructure
Skills after ML infrastructure
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: AWS Lambda | 2 |
| Before: ci/cd | 1 |
| Before: DynamoDB | 1 |
| Before: Eureka | 1 |
| Before: React Native | 1 |
| Before: Spring Boot | 1 |
| Before: dashboard | 1 |
| Before: AWS SNS | 1 |
| After: computer vision | 2 |
| After: software architecture | 1 |
| After: robotics | 1 |
| After: object detection | 1 |
| After: position estimation | 1 |
| After: ML models | 1 |
| After: deploy ML models | 1 |
| After: fisheye cameras | 1 |
Roles most likely to require ML infrastructure
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Tech Lead Manager | 1 | 3.8% |
| ML Ops Engineer | 1 | 2.7% |
| ML Research Engineer | 1 | 2.6% |
| Data Engineering Manager | 5 | 2.1% |
| AI Researcher | 2 | 1.7% |
| Research Engineer | 8 | 1.2% |
| Machine Learning Engineer | 16 | 0.7% |
| AI Architect | 2 | 0.6% |
| Engineering Director | 1 | 0.5% |
| Staff Engineer | 1 | 0.5% |
Roles with the most ML infrastructure postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Software Engineer | 57 | 34.5% |
| Machine Learning Engineer | 16 | 9.7% |
| Engineering Manager | 12 | 7.3% |
| Generalist Engineer | 8 | 4.8% |
| Research Engineer | 8 | 4.8% |
| ML Engineer | 7 | 4.2% |
| Product Manager | 6 | 3.6% |
| Data Engineering Manager | 5 | 3.0% |
| Data Scientist | 4 | 2.4% |
| Backend Data Engineer | 3 | 1.8% |
Top companies posting jobs requiring ML infrastructure
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| 22 | |
| CLERA | 11 |
| Moniepoint | 8 |
| Spotify | 5 |
| Chalk | 4 |
| Monolith | 3 |
| Capital One | 3 |
| Preply | 3 |
| Blend | 3 |
| Turing | 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 ML infrastructure
| Name | Postings | Share |
|---|---|---|
| San Francisco | 17 | 10.3% |
| London | 9 | 5.5% |
| Munich | 8 | 4.8% |
| New York City | 8 | 4.8% |
| Paris | 6 | 3.6% |
| Palo Alto | 5 | 3.0% |
| Singapore | 5 | 3.0% |
| Sunnyvale | 4 | 2.4% |
| Bellevue | 3 | 1.8% |
Skills commonly paired with ML infrastructure
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 2026-10-29. 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 ML infrastructure by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s ML infrastructure postings by all ML infrastructure 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 2,482 identified employers and covers 71% of earlier and 70% of latest indexed postings. Windows: 2026-06-30 to 2026-09-28 and 2026-07-28 to 2026-10-26 (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
- 5a7e889723902f9b
- data_as_of
- 2026-10-29
- window_days
- 90