46.5% of AI Startup Jobs Don't Require Writing Code
By Fast AI Startup Jobs
There is a persistent myth that working in AI means writing code. If you can't train a model or ship a feature, the thinking goes, there's no seat for you at the table.
We decided to test that assumption with data.
We analyzed 29,215 open roles across 1,044 AI startups tracked on Fast AI Startup Jobs. We categorized every single role by function, seniority, and type. The results surprised us — and they should change how you think about building a career in the AI industry.
The Big Picture: Engineering vs. Everything Else
Of all open roles, 53.5% are Engineering, Product, or Research positions. That's the majority, yes — but it means 46.5% of all AI startup jobs are non-engineering roles.
That's not a rounding error. That's nearly half the industry.
The single largest non-engineering category? Go-to-Market (GTM) and Customer Success, which accounts for 22.2% of all open positions — 6,479 roles.
To put that in perspective: there are 1,620 open Account Executive positions across our dataset. That's more than 3x the number of ML Engineer roles (516). If you're a strong salesperson who understands technology, the AI industry needs you more than it needs another machine learning specialist right now.
GTM Grows Fast When Startups Hit Series A
One of the most striking patterns in the data is how GTM hiring changes by funding stage.
At the Seed stage, GTM roles make up just 13.8% of a typical startup's open headcount. The company is still building. Most hires are engineers and researchers. That makes sense.
But by Series A, GTM jumps to 22.8% — nearly doubling its share. This is the inflection point. Once a startup has a product with early traction, the urgent question shifts from "can we build it?" to "can we sell it?"
This pattern holds across industries and geographies. Series A is when the go-to-market machine gets built, and the hiring data proves it.
Not All AI Companies Are Built the Same
The split between engineering and GTM varies wildly depending on the company's business model:
| Company | Total Roles | GTM % | Engineering % | Business Model |
|---|---|---|---|---|
| MaintainX | — | 59% | — | Industrial SaaS |
| OpenAI | — | 11% | — | Platform / Research |
| Anduril | — | 4% | — | Defense Hardware |
MaintainX, which sells maintenance workflow software to industrial customers, has 59% of its open roles in GTM. They need field sales reps who can walk into a factory floor, understand the pain points, and close a deal.
OpenAI, by contrast, has only 11% GTM — their product largely sells itself through an API and a consumer app. The hiring is overwhelmingly technical.
Anduril, building defense hardware, sits at just 4% GTM. When your customer is the U.S. Department of Defense, you don't need a large outbound sales team. You need engineers and government-relations specialists.
The lesson: the type of AI company matters far more than the fact that it's an "AI company."
The Industry Map: Where GTM Dominates
When we zoom out to entire industry categories, the patterns get even clearer:
| Industry Category | GTM Share |
|---|---|
| Industrial Software | 58.7% |
| Cybersecurity | 37.4% |
| FinTech | ~30% |
| Developer Tools | ~20% |
| Quantum Computing | 0% |
Industrial Software leads at 58.7% GTM. These companies sell complex, high-touch products to non-technical buyers. Every deal requires demos, pilots, on-site visits, and relationship-building. You can't automate that with a landing page.
Cybersecurity comes in at 37.4%, and for good reason. Selling security products is inherently trust-based. A CISO isn't going to deploy your AI-powered threat detection tool based on a free trial alone. They need to trust the vendor, understand the implementation, and feel confident about the support. That takes skilled salespeople and customer success managers.
Quantum Computing sits at 0% GTM — these companies are still in pure R&D mode with no commercial product to sell yet.
Geography Matters More Than You Think
We broke down GTM share by city, and the results were dramatic:
| City | GTM Share |
|---|---|
| Chicago | 65.1% |
| New York | ~35% |
| San Francisco | ~20% |
| Seattle | 7.3% |
Chicago leads with a staggering 65.1% GTM share. This makes sense: Chicago's tech scene is historically rooted in enterprise SaaS (Salesforce commerce, Grubhub, Groupon's legacy). The AI startups based there are selling to enterprises, not building foundation models.
Seattle sits at just 7.3%. The city is home to Amazon, Microsoft, and a cluster of deep-tech AI companies. It's an engineering town.
If you're in sales or customer success and want to work in AI, consider looking beyond the Bay Area. Cities like Chicago, New York, and Austin have a disproportionately high number of GTM roles at AI startups.
The Bridge Roles: 1,851 Hybrid Positions
Between pure engineering and pure sales, there's a thriving middle ground. We found 1,851 "bridge" roles — positions that require both technical depth and customer-facing skills:
- Solutions Engineer
- Forward Deployed Engineer
- Sales Engineer
- Technical Account Manager
These roles are gold for people who are technical but don't want to spend their career writing code. Or for salespeople who are willing to get technical.
Databricks stands out here: they have 374 hybrid roles, representing 30.5% of their total headcount. Databricks sells a complex data and AI platform to sophisticated technical buyers. You can't sell it without understanding it, and you can't support it without talking to customers. Hence the massive investment in bridge roles.
The Seniority Advantage in GTM
Here's a data point that should get the attention of anyone in sales or business development:
GTM has 35.7% management-level roles, compared to just 12.5% in engineering.
At the C-Level, the gap is even wider: 8.8% of GTM roles are C-Level (CRO, CMO, VP Sales) versus 3.0% in engineering (CTO, VP Engineering).
What does this mean? GTM has a steeper leadership pyramid. If you're good, you can move into management and executive roles faster in sales and customer success than in engineering. The career progression is faster, and the comp at senior levels is comparable (or higher, with commissions and accelerators).
Your Action Plan
If You're an Engineer Considering a Transition
The bridge roles are your on-ramp. Look for Solutions Engineer or Sales Engineer positions at companies like Databricks, Snowflake, or any of the AI infrastructure companies. You keep your technical skills relevant while developing customer-facing muscles. The pay is often comparable to pure engineering roles, and the path to leadership is shorter.
If You're in Traditional Sales
Your skills are in massive demand — you just need to pick the right companies. Target Series A and beyond companies in Industrial Software, Cybersecurity, or FinTech. These companies need people who can run complex, multi-stakeholder sales cycles. Don't worry about not having a PhD in machine learning. They need you to understand the customer's problem, not the model's architecture.
Look at cities beyond San Francisco. Chicago, New York, and Austin have outsized GTM hiring in AI.
If You're New to the Industry
Start with customer success. It's the lowest barrier to entry, and it gives you a front-row seat to how AI products work in practice. You'll learn the product deeply, build relationships with technical teams, and develop the customer empathy that makes great salespeople.
From customer success, the paths diverge: you can go into account management (more revenue-focused), product management (more product-focused), or solutions engineering (more technical). All of these are well-paid, high-growth career tracks inside AI companies.
The Bottom Line
The AI industry is not just for engineers. Nearly half of all open roles at AI startups are in sales, marketing, customer success, operations, and other non-engineering functions. The companies that will win the AI race aren't just the ones with the best models — they're the ones that can sell, support, and scale their products.
If you've been sitting on the sidelines because you thought AI was only for people who write code, the data says otherwise. The industry needs you. And it needs you now.
Data sourced from 29,215 open roles across 1,044 AI startups tracked on Fast AI Startup Jobs. Analysis date: March 2026.