ABOUT THE COMPANY

Contrario is building the AI-native recruiting marketplace for high-growth startups.

Startups today rely on fragmented recruiters, slow agencies, and outdated ATS tools. Contrario replaces this with a marketplace powered by ML matching, search infrastructure, and automation systems that route recruiters and candidates to the right roles.

We work with 150+ venture-backed startups including Wispr Flow, Bland, Warp, Listen Labs, and Sieve, and a network of 300+ boutique recruiting agencies (many formerly at Stripe, Ramp, and Palantir).

In the past 9 months we’ve:

• Reached $500K monthly revenue (~$6M ARR run rate)
• Completed 100+ startup hires
• Achieved 98% repeat usage from companies that hire
• Maintained <5% customer churn

Our marketplace fills roles significantly faster than traditional recruiting:

• GTM + Ops: 80%+ fill rate (~45 days)
• Remote LATAM engineers: 90%+ fill rate (<30 days)
• In-person engineers: ~50% fill rate (~60 days)

We’ve raised $2.5M from Y Combinator, Nexus Venture Partners, and angels from Hudson River Trading, Stanford, and OpenAI.

We are scaling aggressively:

• $1M–$2M monthly revenue target in the coming months
• $25M+ ARR run rate by the end of the year
• Thousands of recruiters and hundreds of startups on the platform

The team comes from Stanford, YC startups, BCG, NASA, SAIL, Berkeley, and Northwestern.

ABOUT THE ROLE

We are hiring an Applied AI Engineer to help build the core infrastructure powering Contrario’s marketplace.

You will work directly with the founders to design and ship ML matching systems, recruiter routing algorithms, search infrastructure, and AI-powered workflows that determine how talent flows across hundreds of startups.

This is a founding engineering role with extremely high ownership.

You will:

• Ship production systems used by hundreds of recruiters and startups
• Build the core intelligence layer of the marketplace
• Define the technical architecture and engineering culture
• Work directly with the founders on product and technical decisions

The systems you build will determine who gets hired at some of the fastest-growing startups in the world.

KEY RESPONSIBILITIES

Build ML-driven marketplace systems

Develop ranking and retrieval systems that match recruiters, candidates, and roles using embeddings, structured signals, and marketplace data.

Design search and data infrastructure

Build systems that query and rank candidates, recruiters, and roles across large datasets.

Develop AI-native workflows

Ship automation tools that help recruiters source candidates faster and help companies evaluate talent more efficiently.

Ship full-stack product features

Design and implement features across the entire stack — from database schema and APIs to user-facing interfaces.

Own core marketplace infrastructure

Work on systems that power recruiter routing, candidate evaluation pipelines, and marketplace coordination.

Move extremely fast

Launch features quickly, gather feedback from real users, and iterate rapidly.

REQUIREMENTS

Must-Have

Strong full-stack engineering ability

Experience building production systems using TypeScript and modern web frameworks (Next.js / React).

Comfort working with AI systems

Experience integrating or building systems using:

• LLM APIs
• embeddings
• vector search
• retrieval pipelines

Backend and data systems knowledge

Experience designing APIs and working with relational databases.

Product-minded engineer

You care about UX and building systems that users actually love.

High ownership mindset

You can take ambiguous problems and turn them into working systems without heavy direction.

Startup velocity

You ship quickly and are comfortable working in fast-moving environments.

Nice-to-Have

Experience building:

• Search or recommendation systems
• Marketplace products
• AI-native developer workflows

Experience with:

• Supabase / Postgres
• Vercel deployments
• LLM-powered product features

TECH STACK

Frontend
Next.js, React, TypeScript, Tailwind

Backend
TypeScript APIs

Database
Supabase (Postgres)

Infrastructure
Vercel

AI / Data
LLMs, embeddings, retrieval pipelines

1. Chat with CTO
2. Technical Round 1 (30m)
3. Take-Home Project
4. Work Trial (Onsite)