AI & data companies

A small market.
Found by what they built.

Your AI recruiting team ranks ML, data and annotation people on public work, pitches the problem, tests SQL, Python and reasoning, and hands you a shortlist with evidence.

Trusted byDeccan AI
Reasoning report · ML engineerreport
Model evaluation88
Python84
SQL79
Experiment design82
Communication90
Evidence attached to every score
SQL round doneWindow functions · strong
Take-home receivedNotebook · GitHub

Trusted by hiring teams at

WalmartBMWBooking.comRazorpayDocuSignPaytmRandstad DigitalKPI PartnersCodeRabbitDeccan AISpace MatrixPhilogenUnifi CapitalenParadigmphamaxMastercomAnthriqSolinasPopular Motor CorporationPopular Motor VenturesStelloSurf

In AI hiring the résumé says everything and proves nothing. EmployLabs ranks people on what they have published and shipped, reaches them, tests the thinking and shortlists the few.

Who you hire

Research, engineering and data operations.

The people behind a model are not one profile.

ML & research

Judge depth, not vocabulary.

ML engineerResearch engineerApplied scientistNLP engineer

Data engineering

Pipelines, SQL and warehouses, tested in the editor.

Data engineerAnalytics engineerMLOps engineerData platform lead

Data operations

Quality, annotation and domain experts at scale.

Annotation leadQuality analystDomain expert (language, law, medicine)Project coordinator
What slows you down

Strong people, hard to see.

Buzzwords on every profile

Everyone lists transformers. Few show the work behind them.

Research leads stuck on screens

The people who can judge depth are the busiest in the company.

Domain experts in bulk

Annotation and review teams need many careful people, fast, with proof of English and attention.

How it works

From model need to finalist.

Three agents, written for a team that trains and ships models.

MeeraAI recruiter
Meet Meera
Find

Meera maps who builds what you need

Describe the model work in a sentence. She shows who is hiring for it and where the people sit, then ranks matches from papers, repos and public projects.

YOU NO LONGER
  • skim publication lists
  • guess who is hireable
Meera maps who builds what you need
ZiaOur career strategist
Meet Zia
Reach

Zia writes to the researcher, not the résumé

A pitch matched to the problem and the team, answers on salary band and remote policy from your job settings, and a channel switch when email goes quiet.

EmailWhatsAppSMSCallXReddit
YOU NO LONGER
  • draft one-off notes
  • follow up by hand
Zia · ML engineerZia · every channel
Zia

Hi Mira, a role building evaluation tooling for an AI team. Remote in India.

Mira

Is it research or product work?

Zia

Product work, shipping eval pipelines. The salary band and stack are in the brief.

Zia

Booked: interview Tuesday 17:30

NairaAI interviewer
Meet Naira
Interview

Naira tests SQL, Python and reasoning

SQL and Python read and fixed in an editor Naira sees, a take-home from GitHub, and technical questions with follow-ups built from your role. Spoken English can be rated too.

YOU NO LONGER
  • hand-mark data tests
  • pull researchers into screens
Take-home review · PythonGitHub take-home · never run
1def precision_at_k(ranked, relevant, k):2    top = ranked[:k]3    hits = sum(1 for r in top if r in relevant)4    return hits / k56# what if k is larger than the list?
Graded Done · voice follow-up next
ELYouThe final call
See a sample report
Decide

You read evidence, then meet

Overall score, one alignment level, a recommendation and quotes tied to the moment. Share the shortlist with a research lead by link.

YOU NO LONGER
  • ask the same basics twice
  • chase panel feedback
Naira report cardVoice · Naira

Maya ThompsonApplied for Collections SpecialistInterviewed Sep 27, 2026, 38 min

74.0/ 100 overall

AlignedIntegrity 96%English (CEFR) B2
“Clear, calm caller who secured a payment plan without pressure tactics; audit caught 4 of 5 compliance slips. Speaks fluently at a natural call pace.”

Handle a Customer Who Disputes a Past-Due Balance

M2Voice only
Module Score4.1 / 5

Strengths

  • Verified identity before discussing the balance
  • Offered a concrete 3-month plan unprompted

Weaknesses

  • Slight hesitation stating the exact due date
  • Did not repeat the total balance for confirmation

Evidence (transcript quotes)

  • “I understand this is frustrating — let's find a plan that works for you and confirm it today.”
Inside the flow

Made for technical depth.

From the talent map to a shortlist your research lead trusts.

Market

See the talent map

Meera shows who is hiring and where the people are.

ML engineers, evals, Bangalore
PyTorchRLHFEvalsSparkAirflowNLP
SQL

SQL in the editor

Joins and windows read and fixed in an editor Naira sees. Never run.

SELECT user_id, AVG(score) OVER (w)FROM evalsWINDOW w AS (...)Window bug found
Reach

Pitched on the problem

Zia writes on email, X, Reddit and WhatsApp, then books.

Zia
Shortlist

Strengths by module

Per-module strengths and gaps, shared with a lead by link.

Modeling86
Python82
SQL77
Communication90
Spend

Budget cap per role

Spending stops at the cap you set.

Budget1240
creditsCap 2,000
Integrity

Proctored rounds

Tab switches and copy-paste are flagged.

Tab switches
Copy-paste
Integrity score You
Before & after

Hiring for AI teams

The usual way
Buzzwords on every résumé
Researchers ignore generic notes
Researchers run first calls
Bulk hiring spend nobody capped
Gut feel on depth
ELWith EmployLabs
Matches ranked on public work
Zia pitches the problem, then follows up
Naira runs the technical round
A budget cap per role; résumé scoring is free
A shortlist with evidence quotes per module
By the numbers

The engineering behind every hire.

Sourcing800M+professional profiles to search
Engagement5M+candidate conversations on every channel
Interviews50K+AI interviews completed
Shortlists10K+shortlists delivered
Time saved200K+recruiter hours saved
Coverage40+industries hired for
FAQ

We have the answers

How it works, what it costs, and what your candidates see.

How does EmployLabs help AI companies hire?

EmployLabs gives you an AI recruiting team: Meera ranks AI and data candidates from their profiles and public work, with a reason for each score. Zia engages them on six channels and books interviews. Naira interviews by voice with code and document viewers and a GitHub repo viewer, or grades a take-home, and the report shows evidence for every score.

Can Meera find researchers, not just engineers?

She searches professional profiles across every role and ranks on public work, with the profile lines behind each score.

How do you test SQL?

Candidates read and fix SQL in a code editor Naira sees and explain each change by voice. Queries are never run; the reasoning is scored with the rest of the interview.

Can we assess a notebook or repo?

Yes. A take-home can be handed in as a public GitHub repo or ZIP, graded Done, Partial or Not done, then discussed in a short voice follow-up.

We hire reviewers in bulk. Does that work?

Upload résumés to score them at 0 credits, then interview the strongest with a single plan. Spoken English can be rated (CEFR) in the same interview.

Can candidates cheat on technical rounds?

Sessions are proctored. Tab switches, hidden tabs, copy-paste and leaving full screen are flagged, and each report has an integrity score.

Who sees the recordings?

Only reviewers you authorise. Hiring data is never sold or shared.

How does Zia reach researchers?

With a pitch about the problem and the team, on email, X, Reddit, WhatsApp, SMS or a call. She answers salary band and remote questions from your job settings and books the interview.

Let's talk
Still have questions? We're here to help.

Hiring for several roles, or need something specific? We'll walk you through it.

Talk to a specialist (opens in a new tab)

Your next hire is
already out there.

Tell us who you need. We find, contact and interview them, and you meet the best.

The EmployLabs hiring board