AI for Engineering

AI Engineering Jobs: Live Listings and Hiring Trends

AI-skilled engineering roles are growing faster than the broader market. Here's what's hiring right now, who's hiring most, and what they're paying.

AI-skilled engineering roles are growing as a share of total engineering hiring in every quarter we've measured. The hiring is concentrated at four kinds of companies, and the candidates who stand out share a small set of traits.

The bigger picture: If you're an engineer not yet shipping AI, the gap is widening by the week. The two-month investment to learn RAG, basic agent patterns, and one eval framework changes which jobs you can apply to. Engineers who ship one production AI feature, even small, become a different candidate.

AI engineering jobs hiring right now

AI adoption by industry showing hiring intensity

AI Pulse tracks 3,897 job postings across all functions. Of those, 27% of engineering postings mention AI as a required or preferred skill. We refresh the dataset weekly.

The listings below are live and AI-skilled. Click through for the full description, salary band, and apply link.

Who's hiring AI-skilled engineering pros

The companies hiring most aggressively for AI-skilled engineering roles fall into four buckets:

  1. AI labs and foundation model companies. Anthropic, OpenAI, Google DeepMind, Meta AI, and adjacent companies are building out their engineering functions to support enterprise rollouts. They pay top of market and expect candidates to already use AI fluently.
  2. AI-native scale-ups. Companies built around AI from day one (Glean, Hex, Writer, Cursor, Perplexity, Cresta, Harvey, Decagon) are scaling their engineering teams. They're often the best comp-to-equity tradeoff for ambitious candidates.
  3. Big tech AI orgs. Google Cloud AI, AWS Bedrock, Microsoft AI, Apple AIML are hiring engineering pros to support their AI products. These roles offer stability plus AI exposure inside an established company.
  4. Public companies retooling. Stripe, Salesforce, ServiceNow, and others are rebuilding their engineering functions around AI. The roles often pay less than scale-ups but offer scope and platform.

Common requirements in AI engineering job descriptions

The most-cited requirements in AI-skilled engineering postings, in order of frequency:

What's notably absent from most engineering postings: ML PhD, Python expertise, deep math. AI roles outside engineering rarely require those.

What AI engineering jobs are paying

Based on AI Pulse's salary data, AI-skilled engineering roles pay 58% more than non-AI engineering roles. Median total compensation:

For the full salary breakdown including geo cuts and top-paying companies, see the salary page.

What gets you to the interview round

For AI-skilled engineering roles, three things move you up the stack:

  1. A specific AI workflow you've shipped. One example with metrics beats five vague ones. Lead with this in your application materials.
  2. Tool-specific fluency. Name the AI tools you use, what you use them for, and what you'd do differently if you started over.
  3. An eval or quality story. Almost no one mentions how they evaluate AI output quality. Bringing it up signals seniority.

For the full transition path including comp at each level, see the career path page.

What this looks like in practice

Here's the kind of shipped work that gets an AI-skilled engineering pro to the top of the application stack:

A senior backend engineer at a fintech rebuilt the company's customer support tooling around an internal RAG system over the help center, ticket history, and product docs. The system uses Claude with a custom retrieval layer and an eval framework that tests against 200 historical tickets weekly. Tier 1 deflection rose from 8% to 34% in a quarter. The engineer wrote up the architecture and eval design publicly, which led to a senior staff offer at an AI-native scale-up at +50% comp.

The pattern matters more than the specific tools or numbers. Documented work, measurable outcomes, and a story you can tell externally are the three things that move engineering pros from median to top quartile in 2026.

How jobs fits into the bigger engineering picture

Jobs is one piece of the AI-for-engineering story. The full picture covers what AI is changing about the work (the risk page), the skills employers want (the skills page), the tools AI-fluent pros use (the tools page), what the work pays (the salary page), where the hiring is happening (the jobs page), the curriculum to close any gaps (the learn page), and the career path that connects them (the career page).

Most engineering pros end up reading three or four of these pages before they make a move, because the questions are connected. The skills you need depend on the role you're targeting; the salary band depends on the seniority and company type; the curriculum that gets you there depends on what you're starting from. The hub at /ai-for-coding/ ties the pieces together with the strategic synthesis: what's actually happening in engineering, what to do about it, and how to think about your next move.

If you're early in the process, start with the risk page for the honest read on what AI is and isn't changing in engineering. If you're closer to a job move, the jobs page and career page are the highest-impact reads. If you're trying to grow inside your current role, the learn page is the practical sequence.

FAQ: Jobs for Engineering in 2026

The questions below come from engineering pros at every stage, junior to executive. If you don't see yours, the related pages link out to the deeper coverage on each topic.

How many AI engineering jobs are open right now? +

AI Pulse tracks roughly 1,052 live AI-skilled engineering postings at any given time, drawn from a 3,897-job dataset that refreshes weekly. The trendline is up across every quarter we've measured.

Where do I apply to AI engineering jobs? +

Browse the AI Pulse job board for live AI-skilled engineering postings. AI labs, AI-native scale-ups, big tech AI orgs, and public companies retooling around AI are the four buckets hiring most actively.

Do AI engineering jobs require an ML background? +

Usually no. Outside of AI engineering specifically, most AI roles want working fluency with AI tools, not ML credentials. Domain expertise plus AI literacy is the winning combination.

Are AI roles mostly remote? +

It depends on the company type. AI labs lean hybrid in SF, NYC, or London. AI-native scale-ups lean remote. Public companies vary. Remote AI-native is often the best pay-per-hour option.

What if I'm a strong engineering pro without AI experience? +

Pick one AI tool from the tools page, build a workflow that maps to your existing job, document the result, and add it to your resume. Most candidates skip this step. The few who don't move ahead fast.

Related pages on AI for Engineering

The pages below cover the rest of the picture. Each one is a self-contained answer to a different long-tail question. Most engineering pros end up reading three or four before they apply somewhere or make their next move.

How AI Pulse data is built

Every number on this page comes from a continuously updated dataset of 22,351 weekly job postings across 42 roles and 14 industries. Salary figures are derived from postings that disclose compensation and weighted by seniority, location, and remote status. AI penetration percentages reflect the share of postings in each function that explicitly require or prefer AI skills. Premium calculations compare median compensation for postings tagged AI-skilled against postings in the same function and seniority without AI requirements. The dataset refreshes every Sunday; the snapshot used for this page is dated the week shown above.

Sources & notes. Source dataset: AI Pulse weekly job posting index (n=22,351). Salary disclosure rate: 6.4% of postings include compensation. Premium calculations require minimum n=20 postings per role-seniority cell. Updated weekly. For methodology questions, see the About page.

Last updated: 2026-05-23.

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