The AI job market in 2026 is maturing but still growing. After the explosive demand of 2023-2024 driven by ChatGPT and generative AI, the market has shifted from experimentation to production deployment. Companies are hiring not just for AI research, but for the infrastructure, operations, and product skills needed to ship AI products at scale.

AI Pulse analyzes 11,520 active job postings to surface the signals that matter: which skills are growing, which roles are emerging, and where compensation is heading. Our data comes from Indeed, LinkedIn, Greenhouse, Lever, and direct company career pages, refreshed weekly to reflect current market conditions.

What We Track

AI market intelligence showing trends, funding, and hiring velocity

Our market intelligence covers several dimensions: skills and tools (which technologies appear most frequently in job requirements), role distribution (the balance between different AI job categories), work arrangements (remote vs. hybrid vs. on-site), and salary trends (how compensation is moving across roles and locations).

Unlike salary surveys that rely on self-reported data from months ago, our insights come directly from active job postings. This gives you a real-time view of what employers are looking for and willing to pay, not what they were hiring for last quarter.

How Much of the Job Market Now Requires AI

Across 144,643 active postings in every field we track, 18.3% now require AI. Either the role is AI-native or the description calls for AI tools and skills.

45.2% of Engineering jobs now require AI, the most AI-saturated field we track.

Share of jobs requiring AI, by field

Engineering
45.2%
Product
37.5%
Data
35.6%
Marketing
21.3%
Legal
17.7%
Finance
15.4%
People
15.4%
Sales
12.1%
Operations
10.7%
Other
10.6%
Trend: AI requirement across all postings moved from 7.4% (2025-10) to 18.0% (2026-10), 10.6 pts up.

Top AI Tools & Frameworks

Most requested technologies in AI/ML job postings.

Python
2265 (19.7%)
Aws
1144 (9.9%)
Azure
923 (8.0%)
Rag
884 (7.7%)
Gcp
686 (6.0%)
Pytorch
587 (5.1%)
Kubernetes
551 (4.8%)
Claude
549 (4.8%)
Prompt Engineering
462 (4.0%)
Tensorflow
442 (3.8%)
Openai
430 (3.7%)
Docker
420 (3.6%)
Typescript
362 (3.1%)
Langchain
360 (3.1%)
Javascript
272 (2.4%)
Key Insight: Python and PyTorch dominate, with LangChain emerging as the top LLM framework.

Job Categories

Distribution of AI roles by category.

AI/ML Engineer
8736 (75.8%)
AI Software Engineer
595 (5.2%)
AI Product Manager
489 (4.2%)
Data Scientist
454 (3.9%)
Research Engineer
294 (2.6%)
Research Scientist
249 (2.2%)
MLOps Engineer
165 (1.4%)
AI Architect
160 (1.4%)
AI Agent Developer
137 (1.2%)
Data Engineer
84 (0.7%)
AI Safety
60 (0.5%)
Prompt Engineer
36 (0.3%)
LLM Engineer
25 (0.2%)
AI Engineering Manager
23 (0.2%)
AI Consultant
10 (0.1%)

Remote Work Distribution

Work arrangement preferences in AI roles.

onsite
10774 (93.5%)
remote
676 (5.9%)
hybrid
70 (0.6%)

Web_Personalization

Optimizely
3 (0.0%)
Instapage
3 (0.0%)

ml_frameworks

Pytorch
587 (5.1%)
Tensorflow
442 (3.8%)
Transformers
117 (1.0%)
Jax
85 (0.7%)
Keras
48 (0.4%)

languages

Python
2265 (19.7%)
Typescript
362 (3.1%)
Javascript
272 (2.4%)
Rust
81 (0.7%)
Golang
27 (0.2%)

CRM_Enterprise

Salesforce
157 (1.4%)
Dynamics 365
22 (0.2%)
Oracle Cx
1 (0.0%)

llm_providers

Claude
549 (4.8%)
Openai
430 (3.7%)
Gemini
235 (2.0%)
Anthropic
221 (1.9%)
Hugging Face
120 (1.0%)
Llama
58 (0.5%)
Mistral
23 (0.2%)
Cohere
6 (0.1%)

BI_Analytics

Power Bi
189 (1.6%)
Tableau
134 (1.2%)
Looker
45 (0.4%)
Metabase
3 (0.0%)
Thoughtspot
1 (0.0%)
Mode
1 (0.0%)

techniques

Rag
884 (7.7%)
Prompt Engineering
462 (4.0%)
Embeddings
271 (2.4%)
Vector Search
140 (1.2%)
Rlhf
45 (0.4%)
Fine Tuning
35 (0.3%)
Chain Of Thought
1 (0.0%)

infrastructure

Aws
1144 (9.9%)
Azure
923 (8.0%)
Gcp
686 (6.0%)
Kubernetes
551 (4.8%)
Docker
420 (3.6%)
Bedrock
254 (2.2%)
Mlflow
167 (1.4%)
Vertex Ai
156 (1.4%)
Sagemaker
153 (1.3%)
Wandb
1 (0.0%)

llm_frameworks

Langchain
360 (3.1%)
Llamaindex
127 (1.1%)
Crewai
100 (0.9%)
Autogen
99 (0.9%)
Semantic Kernel
92 (0.8%)
Dspy
5 (0.0%)
Haystack
5 (0.0%)

Revenue_Intelligence

Gong
5 (0.0%)

Integration_iPaaS

N8N
55 (0.5%)
Zapier
44 (0.4%)
Mulesoft
15 (0.1%)
Boomi
5 (0.0%)
Make
2 (0.0%)

RevOps_Data_Quality

Demandtools
23 (0.2%)

CRM_MidMarket

Hubspot
32 (0.3%)
Copper
8 (0.1%)
Streak
2 (0.0%)
Monday Sales
2 (0.0%)
Zoho Crm
2 (0.0%)
Pipedrive
1 (0.0%)

Marketing_Automation_Growth

Klaviyo
4 (0.0%)
Mailchimp
3 (0.0%)
Drip
2 (0.0%)
Braze
1 (0.0%)
Iterable
1 (0.0%)

vector_databases

Pinecone
86 (0.7%)
Pgvector
64 (0.6%)
Weaviate
48 (0.4%)
Faiss
35 (0.3%)
Milvus
22 (0.2%)
Qdrant
19 (0.2%)
Chroma
7 (0.1%)

Marketing_Automation_Enterprise

Marketo
7 (0.1%)
Eloqua
2 (0.0%)
Salesforce Marketing Cloud
2 (0.0%)

Gifting_Direct_Mail

Postal
4 (0.0%)

Competitive_Intel

Semrush
13 (0.1%)
Similarweb
1 (0.0%)

Demo_Automation

Walnut
15 (0.1%)
Arcade
1 (0.0%)

Partner_Channel

Reveal
24 (0.2%)

Proposals

Docusign
13 (0.1%)
Ironclad
3 (0.0%)
Concord
2 (0.0%)
Signnow
2 (0.0%)

Customer_Success

Catalyst
43 (0.4%)
Gainsight
2 (0.0%)
Churnzero
1 (0.0%)

Sales_Engagement_Growth

Apollo
12 (0.1%)
Instantly
10 (0.1%)

Lead_Routing

Workato
9 (0.1%)

Review_Platforms

G2
8 (0.1%)

Visitor_Identification

Warmly
5 (0.0%)

Data_Enrichment

Clay
11 (0.1%)

Sales_Enablement_Enterprise

Seismic
5 (0.0%)
Highspot
1 (0.0%)

Revenue_Operations

Atrium
1 (0.0%)

ABM_Advertising

Linkedin Marketing
2 (0.0%)

Reverse_ETL

Fivetran
5 (0.0%)
Airbyte
3 (0.0%)
Hightouch
1 (0.0%)

Video_Prospecting

Loom
6 (0.1%)

Product_Analytics

Amplitude
4 (0.0%)
Posthog
2 (0.0%)
Mixpanel
2 (0.0%)

ABM_Platforms

6Sense
3 (0.0%)

Data_Providers

Zoominfo
8 (0.1%)
Linkedin Sales Navigator
4 (0.0%)
Bombora
2 (0.0%)
Seamless Ai
2 (0.0%)

Meeting_Scheduling

Calendly
2 (0.0%)
Acuity
1 (0.0%)

Chatbots

Cognigy
2 (0.0%)
Ada Support
1 (0.0%)

Conversational_B2B

Chili Piper
1 (0.0%)
Intercom
1 (0.0%)

CPQ

Salesforce Cpq
1 (0.0%)

SDR

Artisan
1 (0.0%)

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How to Use This Data

Market intelligence is only valuable if you act on it. Here's how AI professionals use our data: Career planning: identify which skills to develop based on growing demand, not hype. Salary negotiations: use real benchmarks to anchor compensation discussions. Job search strategy: focus on roles and locations where demand exceeds supply.

2026 Market Outlook

Several trends are shaping the AI job market this year. Production over research: Companies that experimented with AI in 2023-2024 are now hiring for deployment and operations. MLOps, platform engineering, and AI infrastructure roles are growing faster than pure research positions. Specialization matters: Generalist "AI Engineer" roles are giving way to specialists like Prompt Engineers, LLM Engineers, ML Infrastructure Engineers, and AI Product Managers with distinct skill requirements.

Remote remains strong: Despite some companies pushing return-to-office, AI roles maintain higher remote availability than the broader tech market. Our data shows remote AI positions often pay within 5-10% of equivalent on-site roles in major metros. The tools stack is consolidating: After a period of framework proliferation, the market is converging on standard stacks. PyTorch for ML, LangChain for LLM orchestration, and cloud-native deployment.

Our Methodology

We aggregate job postings from Indeed, LinkedIn, Greenhouse, Lever, and company career pages. Each posting is enriched with structured data: job category, required skills, experience level, salary range (when disclosed), location, and remote work type. We update our dataset weekly and filter out duplicates, expired postings, and outliers. Our skill extraction uses both keyword matching and semantic analysis to capture tool mentions accurately.

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