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, categorizing 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.
Engineering vs. everything else
Of all open roles, 53.5% are Engineering, Product, or Research positions. That's the majority, but it means 46.5% of all AI startup jobs are non-engineering roles. Nearly half the industry.
The single largest non-engineering category is Go-to-Market (GTM) and Customer Success, which accounts for 22.2% of all open positions, or 6,479 roles.
To put that in perspective, there are 1,620 open Account Executive positions across our dataset. That's more than three times 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 clearer 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, so most hires are engineers and researchers. That makes sense. 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?"
The pattern holds across industries and geographies. Series A is when the go-to-market machine gets built, and the hiring data shows it.
Not all AI companies are built the same
The split between engineering and GTM varies widely depending on a 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, so 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 type of AI company matters far more than the fact that it's an "AI company."
Where GTM dominates by industry
Zoom out to entire industry categories and the patterns get 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, and 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, which takes skilled salespeople and customer success managers. Quantum Computing sits at 0% GTM, since 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 sharp.
| City | GTM Share |
|---|---|
| Chicago | 65.1% |
| New York | ~35% |
| San Francisco | ~20% |
| Seattle | 7.3% |
Chicago leads at 65.1% GTM share. This makes sense. Chicago's tech scene is historically rooted in enterprise SaaS (Salesforce commerce, Grubhub, Groupon's legacy), so the AI startups based there are selling to enterprises rather than 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 that require both technical depth and customer-facing skills: Solutions Engineer, Forward Deployed Engineer, Sales Engineer, and 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 willing to get technical. Databricks stands out here, with 374 hybrid roles, or 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, which is why they invest so heavily in bridge roles.
The seniority advantage in GTM
Here's a data point worth 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).
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 quicker, and the comp at senior levels is comparable or higher, once you factor in commissions and accelerators.
What the data means for your path
If you're an engineer thinking about 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 come from traditional sales, your skills are in heavy demand. You just need to pick the right companies. Target Series A and beyond companies in Industrial Software, Cybersecurity, or FinTech, where the sales cycles are complex and multi-stakeholder. You don't need a PhD in machine learning. They need you to understand the customer's problem, not the model's architecture. And again, look past 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 there the paths diverge: account management leans more revenue-focused, product management more product-focused, and solutions engineering more technical. All of them are well-paid, high-growth tracks inside AI companies.
So the picture is straightforward. Nearly half of all open roles at AI startups sit outside engineering, in sales, marketing, customer success, operations, and adjacent functions. The companies that win the AI race won't only be the ones with the best models. They'll be the ones that can sell, support, and scale what they build. If you assumed AI had no room for you because you don't write code, the numbers say otherwise.
Data sourced from 29,215 open roles across 1,044 AI startups tracked on Fast AI Startup Jobs. Analysis date: March 2026.