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 1,809 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 234,460 active postings in every field we track, 14.0% now require AI. Either the role is AI-native or the description calls for AI tools and skills.

42.9% of Engineering jobs now require AI, the most AI-saturated field we track.

Share of jobs requiring AI, by field

Engineering
42.9%
Product
37.5%
Data
24.7%
Marketing
22.3%
Finance
18.8%
Legal
13.1%
Sales
11.8%
People
11.0%
Operations
8.9%
Other
6.4%
Trend: AI requirement across all postings moved from 0.0% (2025-06) to 13.0% (2026-06), 13.0 pts up.

Top AI Tools & Frameworks

Most requested technologies in AI/ML job postings.

Python
877 (48.5%)
Aws
592 (32.7%)
Azure
458 (25.3%)
Rag
380 (21.0%)
Gcp
364 (20.1%)
Pytorch
277 (15.3%)
Prompt Engineering
266 (14.7%)
Claude
250 (13.8%)
Kubernetes
243 (13.4%)
Tensorflow
225 (12.4%)
Docker
204 (11.3%)
Langchain
189 (10.4%)
Openai
164 (9.1%)
Typescript
129 (7.1%)
Javascript
101 (5.6%)
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
1274 (70.4%)
Data Scientist
145 (8.0%)
AI Software Engineer
132 (7.3%)
AI Product Manager
86 (4.8%)
Research Scientist
60 (3.3%)
AI Architect
23 (1.3%)
Research Engineer
20 (1.1%)
Data Engineer
17 (0.9%)
AI Agent Developer
16 (0.9%)
MLOps Engineer
14 (0.8%)
AI Engineering Manager
9 (0.5%)
AI Safety
7 (0.4%)
AI Consultant
3 (0.2%)
Prompt Engineer
2 (0.1%)
LLM Engineer
1 (0.1%)

Remote Work Distribution

Work arrangement preferences in AI roles.

onsite
1505 (83.2%)
remote
294 (16.3%)
hybrid
10 (0.6%)

techniques

Rag
380 (21.0%)
Prompt Engineering
266 (14.7%)
Embeddings
100 (5.5%)
Vector Search
35 (1.9%)
Rlhf
22 (1.2%)
Fine Tuning
16 (0.9%)

Chatbots

Drift Ai
38 (2.1%)
Forethought
1 (0.1%)
Cognigy
1 (0.1%)

languages

Python
877 (48.5%)
Typescript
129 (7.1%)
Javascript
101 (5.6%)
Rust
22 (1.2%)
Golang
14 (0.8%)

infrastructure

Aws
592 (32.7%)
Azure
458 (25.3%)
Gcp
364 (20.1%)
Kubernetes
243 (13.4%)
Docker
204 (11.3%)
Bedrock
98 (5.4%)
Mlflow
81 (4.5%)
Vertex Ai
81 (4.5%)
Sagemaker
77 (4.3%)

llm_frameworks

Langchain
189 (10.4%)
Crewai
65 (3.6%)
Llamaindex
64 (3.5%)
Autogen
63 (3.5%)
Semantic Kernel
37 (2.0%)
Dspy
3 (0.2%)
Haystack
3 (0.2%)

llm_providers

Claude
250 (13.8%)
Openai
164 (9.1%)
Gemini
97 (5.4%)
Anthropic
88 (4.9%)
Hugging Face
82 (4.5%)
Llama
34 (1.9%)
Mistral
18 (1.0%)
Cohere
2 (0.1%)

vector_databases

Pinecone
51 (2.8%)
Pgvector
30 (1.7%)
Weaviate
23 (1.3%)
Faiss
19 (1.1%)
Milvus
18 (1.0%)
Chroma
12 (0.7%)
Qdrant
5 (0.3%)

CRM_Enterprise

Salesforce
99 (5.5%)
Dynamics 365
10 (0.6%)

ml_frameworks

Pytorch
277 (15.3%)
Tensorflow
225 (12.4%)
Transformers
50 (2.8%)
Jax
39 (2.2%)
Keras
19 (1.1%)

Proposals

Ironclad
1 (0.1%)
Concord
1 (0.1%)
Docusign
1 (0.1%)

BI_Analytics

Power Bi
84 (4.6%)
Tableau
77 (4.3%)
Looker
29 (1.6%)
Thoughtspot
2 (0.1%)
Domo
1 (0.1%)

Integration_iPaaS

N8N
28 (1.5%)
Zapier
22 (1.2%)
Mulesoft
7 (0.4%)
Make
5 (0.3%)
Workato Ipaas
4 (0.2%)
Celigo
1 (0.1%)
Tray
1 (0.1%)
Boomi
1 (0.1%)

CRM_MidMarket

Hubspot
33 (1.8%)
Freshsales
3 (0.2%)
Monday Sales
2 (0.1%)
Zoho Crm
1 (0.1%)

Sales_Training

Second Nature Training
3 (0.2%)
Mindtickle Training
1 (0.1%)

Revenue_Intelligence

Gong
6 (0.3%)
Avoma
1 (0.1%)
Fireflies
1 (0.1%)

Customer_Success

Catalyst
19 (1.1%)
Gainsight
3 (0.2%)

Review_Platforms

G2
4 (0.2%)

Marketing_Automation_Growth

Mailchimp
2 (0.1%)
Klaviyo
2 (0.1%)

Sales_Engagement_Growth

Apollo
10 (0.6%)
Instantly
9 (0.5%)
Orum
1 (0.1%)
Reply Io
1 (0.1%)

Data_Enrichment

Clay
10 (0.6%)

Visitor_Identification

Warmly
5 (0.3%)

Lead_Routing

Workato
4 (0.2%)
Tray Io
1 (0.1%)

RevOps_Data_Quality

Demandtools
14 (0.8%)

Product_Analytics

Amplitude
3 (0.2%)
Heap
2 (0.1%)
Mixpanel
2 (0.1%)
Pendo
1 (0.1%)

Data_Providers

Zoominfo
3 (0.2%)
Linkedin Sales Navigator
2 (0.1%)
Seamless Ai
1 (0.1%)
Cognism
1 (0.1%)
Lusha
1 (0.1%)

CPQ

Salesforce Cpq
2 (0.1%)

Demo_Automation

Walnut
5 (0.3%)

ABM_Platforms

6Sense
3 (0.2%)

Marketing_Automation_Enterprise

Marketo
6 (0.3%)
Salesforce Marketing Cloud
3 (0.2%)
Hubspot Marketing
1 (0.1%)

Partner_Channel

Reveal
12 (0.7%)

Gifting_Direct_Mail

Postal
2 (0.1%)

Meeting_Scheduling

Calendly
1 (0.1%)

SDR

Piper Qualified
1 (0.1%)

PLG_Tools

Pendo Plg
1 (0.1%)

Reverse_ETL

Fivetran
2 (0.1%)
Census Data
2 (0.1%)
Hightouch
1 (0.1%)

Sales_Enablement_Enterprise

Seismic
2 (0.1%)
Mindtickle
1 (0.1%)

Sales_Engagement_Enterprise

Salesloft
3 (0.2%)
Outreach Io
2 (0.1%)

Competitive_Intel

Semrush
3 (0.2%)

Revenue_Operations

Aviso
1 (0.1%)

ABM_Advertising

Linkedin Marketing
1 (0.1%)

Web_Personalization

Unbounce
2 (0.1%)

Video_Prospecting

Loom
1 (0.1%)

Conversational_B2B

Intercom
1 (0.1%)
Default Gtm
1 (0.1%)

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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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