Skillenai

synthetic data generation jobs in 2026 — demand, top roles hiring, and related skills

As of 2026-10-30, synthetic data generation appears in 270 job postings indexed by Skillenai over the past 90 days — Research Engineer has the most postings mentioning synthetic data generation, with demand share up 12.5% vs the prior 4 weeks.

Last updated · 90d ending 2026-10-30

Postings · last 90 days
270
Demand vs prior month
up 12.5% vs the prior 4 weeks
Top role · 13.0% of skill postings
Research Engineer
Top hiring metro
San Francisco

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Frequently asked questions about synthetic data generation

+Is synthetic data generation in demand in 2026?

Yes. synthetic data generation appears in 270 job postings indexed by Skillenai over the 90 days ending 2026-10-30. Research Engineer accounts for the most postings mentioning synthetic data generation (13.0% of all postings mentioning synthetic data generation).

+What jobs require synthetic data generation?

According to the Skillenai jobs index over the 90 days ending 2026-10-30, among roles with at least 20 postings, the highest shares mentioning synthetic data generation are AI Research Scientist (9.1% of that role’s postings mention synthetic data generation), AI Research Intern (6.2% of that role’s postings mention synthetic data generation), Member of Technical Staff (5.7% of that role’s postings mention synthetic data generation).

+What skills are commonly paired with synthetic data generation?

Across job postings indexed by Skillenai (90 days ending 2026-10-30), synthetic data generation most often appears alongside Python, PyTorch, Reinforcement learning, machine learning, fine-tuning.

+Where is synthetic data generation most in demand?

As of 2026-10-30, the metro areas posting the most jobs requiring synthetic data generation are San Francisco, New York City, Santa Clara, London, Mountain View, according to the Skillenai jobs index.

+How can I keep up with new synthetic data generation content and jobs?

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

+Which skills come before and after synthetic data generation?

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 synthetic data generation — last 90 days

Salary distribution

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

Career paths around synthetic data generation

Skills documented before and after this skill across employer changes.

Historical career profiles · all locations

Skills before synthetic data generation

Before synthetic data generationXSS → synthetic data generation: 1 observed employer moves with this skill pairmodel prediction → synthetic data generation: 1 observed employer moves with this skill pairdata lineage documentation → synthetic data generation: 1 observed employer moves with this skill pairDynamoDB → synthetic data generation: 1 observed employer moves with this skill pairmetadata → synthetic data generation: 1 observed employer moves with this skill pairpython → synthetic data generation: 1 observed employer moves with this skill pairData Science → synthetic data generation: 1 observed employer moves with this skill pairAWS Glue → synthetic data generation: 1 observed employer moves with this skill pairsyntheticdatagenerationXSS: 1 movesXSS1 movesmodel prediction: 1 movesmodel prediction1 movesdata lineage documentation: 1 movesdata lineagedocumentation1 movesDynamoDB: 1 movesDynamoDB1 movesmetadata: 1 movesmetadata1 movespython: 1 movespython1 movesData Science: 1 movesData Science1 movesAWS Glue: 1 movesAWS Glue1 moves

Skills after synthetic data generation

After synthetic data generationsynthetic data generation → feature extraction: 1 observed employer moves with this skill pairsynthetic data generation → retrieval: 1 observed employer moves with this skill pairsynthetic data generation → pagination: 1 observed employer moves with this skill pairsynthetic data generation → docker: 1 observed employer moves with this skill pairsynthetic data generation → Flask API: 1 observed employer moves with this skill pairsynthetic data generation → Isolation Forest: 1 observed employer moves with this skill pairsynthetic data generation → Random Forest: 1 observed employer moves with this skill pairsynthetic data generation → AWS: 1 observed employer moves with this skill pairsyntheticdatagenerationfeature extraction: 1 movesfeature extraction1 movesretrieval: 1 movesretrieval1 movespagination: 1 movespagination1 movesdocker: 1 movesdocker1 movesFlask API: 1 movesFlask API1 movesIsolation Forest: 1 movesIsolation Forest1 movesRandom Forest: 1 movesRandom Forest1 movesAWS: 1 movesAWS1 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: XSS1
Before: model prediction1
Before: data lineage documentation1
Before: DynamoDB1
Before: metadata1
Before: python1
Before: Data Science1
Before: AWS Glue1
After: feature extraction1
After: retrieval1
After: pagination1
After: docker1
After: Flask API1
After: Isolation Forest1
After: Random Forest1
After: AWS1

Roles most likely to require synthetic data generation

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

RolePostings mentioning skill% of role postings mentioning skill
AI Research Scientist79.1%
AI Research Intern26.2%
Member of Technical Staff25.7%
Research Engineer355.2%
Developer Relations Manager25.1%
Machine Learning Research Engineer15.0%
Research Fellow15.0%
Founding AI Engineer14.8%
Applied Scientist104.0%
Customer Program Manager14.0%

Roles with the most synthetic data generation postings

RolePostings mentioning skillShare of skill postings
Research Engineer3513.0%
Software Engineer238.5%
Machine Learning Engineer165.9%
Data Scientist155.6%
AI Engineer145.2%
Research Scientist145.2%
ML Engineer114.1%
Applied Scientist103.7%
AI Research Scientist72.6%
Data Engineer62.2%

Top companies posting jobs requiring synthetic data generation

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

Top companies posting jobs requiring synthetic data generation
CompanyPostings · 90 days
NVIDIA20
CLERA13
Turing13
Mercor6
Truelogic5
Cisco5
General Motors4
Waymo4
Adobe4
Thinking Machines3

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 synthetic data generation

NamePostingsShare
San Francisco2710.0%
New York City114.1%
Santa Clara114.1%
London103.7%
Mountain View72.6%
Munich51.9%
Singapore51.9%
Sunnyvale51.9%
Boston41.5%

Skills commonly paired with synthetic data generation

Python (163)PyTorch (90)Reinforcement learning (70)machine learning (55)fine-tuning (40)model evaluation (36)LLMs (36)deep learning (33)TensorFlow (31)distributed training (30)

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 2026-10-30. 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 synthetic data generation by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s synthetic data generation postings by all synthetic data generation 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
6dcd45def9e5f970
data_as_of
2026-10-30
window_days
90
Compiled by Jared Rand · Data sourced from the Skillenai labor market index