Prepin turns one conversation into a living profile, finds roles worth reviewing, and keeps every intro under your control.
June 27
Reading profile
Where should I look: remote, hybrid, onsite, or specific cities?
Where should I look: remote, hybrid, onsite, or specific cities?
Remote
Hybrid
Onsite
Updating search
What compensation range should I use as a target? A minimum is enough.
$150K+
$180K+
$220K+
Finding roles
Today
4 items
ML Engineer, AI Platform
Microsoft · United States · $139K-$218K
Applied ML Engineer
Square · California, United States · $277K-$415K
GenAI Platform Engineer
Reddit · United States · $293K-$410K

ML Platform Engineer
Databricks · United States · $158K-$265K
Yesterday
3 items
Inference ML Engineer
Snowflake · United States · $200K-$250K
Embeddings Engineer
Reddit, Inc. · United States · $253K-$355K
AI Agent Engineer
Google · Mountain View, United States · $130K-$300K
The day before yesterday
3 items
Machine Learning Engineer
Apple · United States · $150K-$185K

Backend Engineer
Confluent.io · Austin · $100K-$200K
Backend Engineer
LinkedIn · Remote · $100K-$140K
Why Prepin
Prepin interviews you like a great recruiter — goals, skills, tradeoffs — and drafts a recruiter-ready profile for your review.
The Problem
What most tools do
Resumes blasted into cold applications
Keyword filters that miss your strengths
Recruiters see a static profile, not you
What Prepin does
One conversation builds your profile
Skill checks prove what you can do
You approve every intro that goes out
Understand you first, then match.
01 — Talk
One real voice conversation — goals, strengths, dealbreakers.
No forms. No resume parsing.
Profile intake
Prepin asks
Speaking
0:58
Goals
Strengths
Preferences
02 — Profile
It becomes a structured profile they can actually use.
Deeper than a resume.
Candidate Profile
Depth: High
0
Backend
0
Systems
0
API Design
Signal
Owns production APIs and reliability.
Preference
Small teams, remote-first.
Avoid
No pure maintenance roles.
03 — Match
The few roles that truly fit — you choose what to pursue.
No auto-apply. Quality over quantity.
This Week's Matches
Software Engineer · Remote
3 strong fits
Best
AI Platform Engineer
GenAI Engineer

ML Platform Engineer
Inference ML Engineer
ML Engineer, AI Platform
71
0
GenAI Platform Engineer
62
0

ML Platform Engineer
55
0
What Prepin does
PREPIN CANDIDATE AGENT · ACTIVE
3 sources · 3 workflows
CANDIDATE CONTEXT
Résumé
Conversation
DEEP PROFILE
Skills · goals · evidence
PREPIN AGENT
Processing
Best-fit roles
Relevant opportunities
Recruiter outreach
Email + approved intros
Interview practice
Coding · system design · behavioral
›
LinkedIn profile and résumé understood.
CONTEXT
Unified
WORKFLOWS
Personalized
CONTROL
Candidate-first
Results ready for review
How we compare
AI candidate agent
Candidate context
Deep profile from conversation
Keywords only
Only what you provide
Résumé and recruiter notes
Skill signal
Assessment plus evidence
Résumé claims
Drafts, not validation
Résumé and LinkedIn
Match quality
A few high-fit roles
Large, noisy lists
Suggestions only
Limited to open roles
Candidate control
You approve every intro
You do every step
You manage every prompt
Mostly recruiter-led
Spam risk
No mass applications
Low spam, high effort
Easy to overproduce
Often outreach-heavy
Best use
Finding 3–5 strong fits
Browsing and tracking
Drafting content
Filling an open role
Compare
Prepin
Job boards
Candidate context
Deep profile from conversation
Keywords only
Skill signal
Assessment plus evidence
Résumé claims
Match quality
A few high-fit roles
Large, noisy lists
Candidate control
You approve every intro
You do every step
Spam risk
No mass applications
Low spam, high effort
Best use
Finding 3–5 strong fits
Browsing and tracking
The model
Conversation
Builds the complete profile
Matches
Only the strongest fits
Auto-applies
You approve every move
Fit score
Reasoning behind every match
FAQ
No auto-apply · No mass submissions · Candidate-controlled intros
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