distributed training jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-09-30, distributed training appears in 680 job postings indexed by Skillenai over the past 90 days — Machine Learning Engineer has the most postings mentioning distributed training, with demand share up 4.7% vs the prior 4 weeks.
Last updated · 90d ending 2026-09-30
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Frequently asked questions about distributed training
+Is distributed training in demand in 2026?
Yes. distributed training appears in 680 job postings indexed by Skillenai over the 90 days ending 2026-09-30. Machine Learning Engineer accounts for the most postings mentioning distributed training (17.5% of all postings mentioning distributed training).
+What jobs require distributed training?
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 distributed training are Machine Learning Infrastructure Engineer (56.2% of that role’s postings mention distributed training), Applied Research Engineer (46.2% of that role’s postings mention distributed training), Applied ML Engineer (38.1% of that role’s postings mention distributed training).
+What skills are commonly paired with distributed training?
Across job postings indexed by Skillenai (90 days ending 2026-09-30), distributed training most often appears alongside PyTorch, Python, JAX, machine learning, TensorFlow.
+Where is distributed training most in demand?
As of 2026-09-30, the metro areas posting the most jobs requiring distributed training are San Francisco, London, New York City, Mountain View, Seattle, according to the Skillenai jobs index.
+How can I keep up with new distributed training content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning distributed training alongside the jobs index. You can subscribe to a daily email digest of new distributed training content from your Skillenai account.
+Which skills come before and after distributed training?
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 distributed training — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around distributed training
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before distributed training
Skills after distributed training
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: Hugging Face Transformers | 1 |
| Before: Time-series forecasting | 1 |
| Before: Apache Kafka | 1 |
| Before: inverse kinematic equations | 1 |
| Before: Power BI | 1 |
| Before: 7-DoF manipulator | 1 |
| Before: aws sagemaker | 1 |
| Before: MLflow | 1 |
| After: exploratory data analysis | 1 |
| After: feature extraction methods | 1 |
| After: high-dimensional data visualization | 1 |
| After: generative adversarial networks (GANs) | 1 |
| After: dataloaders | 1 |
Roles most likely to require distributed training
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Machine Learning Infrastructure Engineer | 18 | 56.2% |
| Applied Research Engineer | 12 | 46.2% |
| Applied ML Engineer | 8 | 38.1% |
| ML Research Engineer | 13 | 27.1% |
| ML Scientist | 6 | 23.1% |
| Machine Learning Researcher | 8 | 21.1% |
| Applied Research Scientist | 5 | 19.2% |
| ML Infrastructure Engineer | 9 | 18.8% |
| Machine Learning Systems Engineer | 5 | 18.5% |
| Machine Learning Research Engineer | 5 | 14.7% |
Roles with the most distributed training postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Machine Learning Engineer | 119 | 17.5% |
| ML Engineer | 61 | 9.0% |
| Research Engineer | 56 | 8.2% |
| Research Scientist | 32 | 4.7% |
| Software Engineer | 30 | 4.4% |
| Machine Learning Infrastructure Engineer | 18 | 2.6% |
| AI Researcher | 13 | 1.9% |
| ML Research Engineer | 13 | 1.9% |
| Applied Research Engineer | 12 | 1.8% |
| Applied Scientist | 11 | 1.6% |
Top companies posting jobs requiring distributed training
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Thinking Machines | 29 |
| NVIDIA | 29 |
| Waymo | 26 |
| Advanced Micro Devices Inc. | 15 |
| Wayve | 15 |
| Cohere | 15 |
| 15 | |
| Jane Street | 14 |
| Synthesia | 14 |
| Graphcore | 12 |
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 distributed training
| Name | Postings | Share |
|---|---|---|
| San Francisco | 84 | 12.4% |
| London | 41 | 6.0% |
| New York City | 38 | 5.6% |
| Mountain View | 33 | 4.9% |
| Seattle | 23 | 3.4% |
| Sunnyvale | 20 | 2.9% |
| San Jose | 19 | 2.8% |
| Singapore | 19 | 2.8% |
| Palo Alto | 15 | 2.2% |
Skills commonly paired with distributed training
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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 distributed training by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s distributed training postings by all distributed training 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 2,596 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
- 286c07130d80afe5
- 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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