AI Startup Hiring Trends – Roles, Functions & Seniority Levels

In Part 1 of this series, I shared an overview of the core product areas where emerging AI startups are innovating, from generative audio and code agents to LLM infrastructure and AI productivity tools.

In this post, I’m digging into the current job openings at these companies to answer a key question:

What roles are in demand, and who are these companies actually hiring right now?

After going through the job pages of 14 high-growth AI startups, I grouped the openings by primary function and seniority level. Here's what emerged.

Key Roles and Seniority Levels in AI Startup Hiring

1. Role Distribution by Function

Across the board, the largest concentration of open roles falls into these five categories:

Engineering (Software, ML, Systems)

This is by far the most active function. Nearly every company is hiring for backend, full-stack, infra, and ML engineers.

Machine Learning & Research

A close second. This includes Research Scientists, ML Engineers, and Applied ML roles  especially in companies working on foundational models or GenAI.

Product & Design

Product roles are fewer but notable, particularly in companies building user-facing dev tools or content-generation products. Product Designers with ML intuition are in demand.

Operations & People

Some companies (especially those in Series A/B) are hiring their first Ops or People team members roles like Talent Partner, Business Operations Lead, or Chief of Staff.

Sales & GTM (Go-to-Market)

Surprisingly limited. Most early-stage AI startups are still product-heavy; only a few have begun building out sales and marketing functions.

“Most early-stage AI startups are still product-heavy; only a few have begun building out sales and marketing functions.”

2. Seniority Breakdown

Startups in this space are leaning heavily toward senior and mid-level hires. Here's the typical distribution:

Senior / Lead / Principal Roles

The majority of engineering and ML roles fall here. Companies want people who can build without hand-holding.

Mid-Level / IC (3–6 YOE)

Common in full-stack and product engineering. Versatile generalists are in high demand.

Junior / Entry-Level

Very rare. Almost none of the 14 companies are hiring fresh grads or junior engineers right now  likely due to the cost and time of onboarding.

Internships

Extremely limited. A few research teams offer internships, but these are highly competitive and often focused on PhD candidates.

“Hands-on builders are valued roles often require end-to-end ownership from designing the architecture to deploying models or APIs.”

3. Key Observations

Hands-on builders are valued

Roles often require end-to-end ownership from designing the architecture to deploying models or APIs.

Hybrid skill sets are preferred

Many roles span traditional boundaries like “ML Engineer with backend infra experience” or “Product Engineer who understands prompt engineering.”

Small, high-ownership teams

Most teams are under 50 people. Startups are hiring individuals who can operate with little structure and build from scratch.

In the next part of this series, I’ll break down which technical skills are most frequently mentioned across job descriptions from programming languages and frameworks to model types and cloud platforms.

If you're a job seeker or hiring manager in the AI space, I hope this gives you a clearer view of where the momentum is.