Fine-tuning and scikit-learn frequently appear together in AI job postings, with 96 current openings requiring both skills. The most common role for this combination is AI Product Manager. Jobs requiring both skills pay up to $250K on average. Below you'll find detailed salary data, hiring companies, and related skill combinations.
Fine-tuning and scikit-learn appear together in 96 current AI job postings, most commonly for AI Product Manager 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 Fine-tuning + scikit-learn
AI Product Manager60 jobs
Research Scientist17 jobs
AI/ML Engineer13 jobs
LLM Engineer4 jobs
Data Scientist1 jobs
Prompt Engineer1 jobs
Career Impact
Professionals who combine Fine-tuning and scikit-learn earn a median salary ceiling of
$250K, compared to $228K for Fine-tuning alone and
$240K for scikit-learn alone. This 7% premium
reflects the market values the integration of these two skills — employers pay more for candidates who can work across both rather than hiring separate specialists.
Top Companies Hiring for Fine-tuning + scikit-learn
Based on our analysis of current AI job postings, 96 positions require both Fine-tuning and scikit-learn skills. The most common role for this combination is AI Product Manager.
Jobs requiring both Fine-tuning and scikit-learn pay an average of $132K to $250K based on 91 postings with disclosed compensation.
Jobs requiring both Fine-tuning and scikit-learn pay $250K on average (max), compared to $228K for Fine-tuning alone and $240K for scikit-learn alone. That represents a 7% premium for having both skills.
The most common roles requiring both Fine-tuning and scikit-learn are: AI Product Manager, Research Scientist, AI/ML Engineer, LLM 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 96 job postings that list both Fine-tuning and scikit-learn
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.
