Current Status
Squarespace doesn't have active AI or ML job postings in our current dataset. Across the market, we're tracking 17,365 open AI roles right now. The most active categories: AI/ML Engineer (15,673), AI Software Engineer (389), AI Product Manager (385). Companies move through hiring cycles. Headcount freezes, reorgs, and budget shifts all affect posting volume. A quiet period doesn't mean a company has abandoned AI investment. Many organizations pause external hiring while restructuring teams or shifting strategy, then ramp back up quickly when priorities align.
AI Compensation Snapshot
The overall median AI salary is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile for AI compensation reaches $300,688.
Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 roles); AI Agent Developer ($252,000 median, 30 roles). The compensation gap between the highest and lowest-paying categories can reach $50K or more at the median, reflecting the premium on specialized technical skills.
Compensation by seniority level: Entry: $85,000 (337 roles); Mid: $147,000 (7,773 roles); Senior: $225,000 (4,846 roles); Director: $230,600 (783 roles); VP: $248,357 (324 roles). The jump from mid-level to senior typically represents a 20-30% increase, while the move to Director or VP adds another 30-50% for roles with disclosed compensation.
Most In-Demand AI Skills
The top skills across all AI job postings: Rag (11,079 postings); Aws (5,976 postings); Rust (4,954 postings); Python (2,760 postings); Azure (1,882 postings). These appear consistently across seniority levels and company sizes, reflecting the core technical requirements of the AI job market.
The AI Job Market Today
The AI job market spans 17,365 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (15,673), AI Software Engineer (389), AI Product Manager (385). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (1,605) are outnumbered by mid-level (10,766) and senior (3,423) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,571 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,271 positions), with 16,034 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (11,079 postings), Aws (5,976 postings), Rust (4,954 postings), Python (2,760 postings), Azure (1,882 postings), Gcp (1,475 postings), Prompt Engineering (923 postings), Pytorch (881 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
AI Hiring Overview
The AI job market has 17,365 open positions tracked in our dataset. By seniority: 1,605 entry-level, 10,766 mid-level, 3,423 senior, and 1,571 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,271 positions). The remaining 16,034 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 roles).
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
The AI Job Market Today
The AI job market spans 17,365 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (15,673), AI Software Engineer (389), AI Product Manager (385). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (1,605) are outnumbered by mid-level (10,766) and senior (3,423) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,571 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,271 positions), with 16,034 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (11,079 postings), Aws (5,976 postings), Rust (4,954 postings), Python (2,760 postings), Azure (1,882 postings), Gcp (1,475 postings), Prompt Engineering (923 postings), Pytorch (881 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Companies Actively Hiring
Frequently Asked Questions
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