Should an ai startup hire a machine learning researcher or an ai engineer first

Matching the Hire to What Most Startups Actually Need

Most early-stage AI startups should generally hire an AI engineer before a machine learning researcher, since building applications on top of existing foundation models — the more common approach for most startups — requires systems and application engineering skill more than original model research capability.

Why Most Startups’ Actual Technical Needs Favor an Engineer

As covered elsewhere, most AI startups build their product by orchestrating existing foundation models — through API integration, retrieval-augmented systems, and application logic — rather than developing new modeling approaches from scratch, meaning the practical, day-to-day technical work most startups need done aligns much more closely with an AI engineer’s skill set than a researcher’s.

What an AI Engineer Typically Contributes at This Stage

An AI engineer focused on building applications on top of existing models can handle the practical work of integrating model APIs, designing retrieval and context systems, building the surrounding application logic, and iterating quickly on the product based on user feedback — the core technical work most early-stage startups actually need to get a working product into customers’ hands.

Why a Researcher’s Skill Set Doesn’t Match Most Early Needs

A machine learning researcher’s core skill — developing new modeling approaches, architectures, or training techniques — generally isn’t the bottleneck for a startup building on existing models, since the underlying model capability is already provided by the foundation model provider, meaning this specialized skill set often goes underutilized relative to its cost at this early stage.

When the Priority Genuinely Reverses

This priority reverses for the narrower set of startups whose core value proposition specifically depends on original model research or a genuinely novel technical approach that existing models don’t support — for these specific cases, an early research hire may be genuinely necessary and well-justified, rather than a mismatch with the startup’s actual needs.

Why Getting This Hiring Priority Right Matters for Limited Early Resources

Given how limited early-stage hiring budgets typically are, correctly matching the first technical hire to the startup’s actual, specific needs — rather than defaulting to whichever role sounds more prestigious or technically impressive — meaningfully affects how efficiently a startup’s limited early resources translate into real product progress.

Bottom Line

Most early-stage AI startups should generally hire an AI engineer before a machine learning researcher, since building on existing foundation models requires application and systems engineering skill more than original research capability — with this priority reversing only for the narrower set of startups whose core value proposition specifically depends on genuinely original model research.

Go deeper

• What is the difference between an ai research scientist and an applied ai engineer?
• What’s the actual difference between AI machine learning and deep learning?
• Should a business build its own custom ai model or use an existing provider api?