Machine Learning jobs and hiring demand

Core and required roles are separated from contextual and optional mentions so the headline does not overstate job intent.

Explore Machine Learning by location

Select up to five markets; results and hiring evidence update together.

1,488core + required roles2,142context + optional mentions741remote-friendly▼ -841 this week

Employer concentration

EPAM accounts for 630 of 3630 mentions (17%). The remaining 3000 mentions span other employers.

Machine Learning market intelligence visualization

Model work connected to product and operational evidence

Machine Learning market guide

Machine-learning demand covers model development, applied engineering, data pipelines, evaluation, platform work, and domain-specific products. The job title alone rarely reveals which part of the lifecycle matters most.

3,630 live roles154 hiring companies20% remote-friendly

Vacancy evidence map

Where Machine Learning carries hiring weight

Each segment represents roles where Machine Learning was parsed at that evidence tier. This keeps a required production skill separate from contextual stack language.

362 Core1126 Required1532 Supporting610 Optional
1,488core + required41% of mentions
23.6roles per company154 employers

Market interpretation

What Machine Learning demand means in practice

ApplyDjinn’s evidence tiers help distinguish roles where ML is the product or primary craft from roles where it is supporting context. Companion technologies show whether employers emphasize experimentation, data engineering, deployment, inference, computer vision, NLP, or generative AI.

Strong applications explain the data, baseline, metric, validation approach, deployment boundary, and monitoring plan. Model choice matters, but evidence of reliable evaluation and product impact usually travels farther than an isolated accuracy claim.

Applied MLML platform / MLOpsData scienceAI product engineering

Reading the market

Signals to look for

  • Python, SQL, cloud, data pipelines, and model frameworks form the recurring foundation.
  • MLOps and platform roles emphasize reproducibility, deployment, observability, and cost.
  • Applied roles often value domain data and product judgment alongside modelling depth.

Questions candidates ask

Machine Learning hiring FAQ

What is the difference between AI and machine-learning vacancies?

AI is a broader market label that can include workflow and product roles; ML vacancies more often require model, data, evaluation, or deployment depth.

What portfolio evidence is useful for ML roles?

A reproducible project with a baseline, honest validation, error analysis, deployment considerations, and a clear account of personal contribution.

How Machine Learning appears in job requirements

Inferred from the title and requirement wording in 3630 active roles; each role is counted once. Read the methodology.

Posting evidence, not a company-wide stack claim
Main / core
10%(362)
Required
31%(1126)
Supporting / context
42%(1532)
Nice-to-have
17%(610)

Skills mentioned with Machine Learning

A shared posting does not always require both skills. Explicit alternatives are counted separately.

Role graph

Roles hiring for Machine Learning