How Agrigator Hired Its Founding ML Engineers with Aptora

Agrigator was hiring ML engineers and needed a better way to tell who could actually do the work.

Live technical interviews were taking up founder time, while most assessment platforms felt too heavy for a company at their stage.

They wanted something simpler:

• No long implementation
• No new integrations
• No extra recruiting system to manage
• A realistic way to evaluate candidates before spending time interviewing them

The Problem

After an initial conversation, Agrigator was still struggling to separate candidates who interviewed well from candidates who were actually strong engineers.

Some candidates had:

• Impressive resumes
• Advanced degrees
• Strong knowledge of ML concepts
• Polished interview answers

But those signals did not always translate into strong engineering work.

The team was relying on live technical interviews to figure that out, which meant spending founder and engineering time screening candidates one by one.

Other assessment tools were an option, but required more setup and integration than Agrigator needed.

The Solution

Agrigator used Aptora immediately after its initial candidate screen and replaced the live technical round entirely.

1. Aptora built the assessment around the actual role

Candidates completed a one-hour assessment focused on improving a RAG pipeline in a GPU-backed environment.

It was designed to test the type of applied ML engineering work Agrigator cared about, rather than a generic coding problem.

2. Candidates worked with AI

Candidates could use an AI agent while solving the problem.

Agrigator could see how they:

• Approached the existing system
• Diagnosed problems
• Used AI
• Tested their changes
• Made technical decisions

3. Agrigator used it without changing the rest of its hiring process

There was no lengthy implementation or new workflow to build.

Agrigator kept its initial chat screen, sent the Aptora assessment to 15 candidates, and moved the strongest candidates forward.

The results changed how the team ranked the candidate pool.

Some candidates who looked strongest on paper were not the strongest performers on the task. Others stood out once they were given a real engineering problem to solve.

“Aptora changed who I would have hired.”

Co-Founder, Agrigator

The Outcome

15 candidates assessed

All 15 ML engineering candidates completed the Aptora assessment after the initial screen.

Live technical round removed

Aptora replaced the live technical interview instead of adding another step to the process.

2 candidates hired

Agrigator hired the two strongest candidates from the process.

Both were still performing strongly six months later

Six months after hiring, Agrigator confirmed that both hires were technically strong on the job.

Agrigator came back for its next hiring cycle

When the company started hiring again, it returned to Aptora.

The Result

For Agrigator, the value was straightforward:

Less founder time spent screening candidates.

No heavy assessment setup or integration.

A clearer view of who could actually do the work.

And most importantly, the hiring signal held up after the candidates joined the team.

“Aptora changed who I would have hired.”

Co-Founder, Agrigator