PyTorch and RAG frequently appear together in AI job postings, with 265 current openings requiring both skills. The most common role for this combination is AI/ML Engineer. Jobs requiring both skills pay up to $224K on average. Below you'll find detailed salary data, hiring companies, and related skill combinations.
PyTorch and RAG appear together in 265 current AI job postings, most commonly for AI/ML Engineer positions. This combination signals that employers are looking for versatile engineers who can work across multiple layers of the AI stack. Companies hiring for this pair tend to value candidates who can bridge different technical domains rather than specialize in just one.
Top Roles Requiring PyTorch + RAG
AI/ML Engineer174 jobs
Data Scientist29 jobs
LLM Engineer10 jobs
MLOps Engineer10 jobs
Research Scientist10 jobs
Research Engineer9 jobs
Career Impact
Professionals who combine PyTorch and RAG earn a median salary ceiling of
$224K, compared to $228K for PyTorch alone and
$224K for RAG alone. This 1% discount
reflects the salary ceiling is roughly equivalent whether you specialize in one skill or combine both, suggesting the market values either path equally.
Based on our analysis of current AI job postings, 265 positions require both PyTorch and RAG skills. The most common role for this combination is AI/ML Engineer.
Jobs requiring both PyTorch and RAG pay an average of $151K to $224K based on 203 postings with disclosed compensation.
Jobs requiring both PyTorch and RAG pay $224K on average (max), compared to $228K for PyTorch alone and $224K for RAG alone. That represents a 1% discount for having both skills.
The most common roles requiring both PyTorch and RAG are: AI/ML Engineer, Data Scientist, LLM Engineer, MLOps Engineer. These positions typically involve building production AI systems that leverage both technologies.
Data Source: Analysis based on 1,969 AI job postings collected and verified by AI Market Pulse. Data reflects active job listings as of March 2026. Salary figures represent posted compensation ranges and may not include equity, bonuses, or other benefits.
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Methodology
Skill co-occurrence data is derived from 265 job postings that list both PyTorch and RAG
as required or preferred skills. Salary data includes only postings with disclosed compensation ranges.
Data is updated weekly from major job boards and company career pages.
