Software Engineer - Platform/Applied AI (Fullstack) San Francisco

AfterQuery; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

AfterQuery is hiring core engineers to design and scale data infrastructure that powers frontier model development. You will own end-to-end projects from conception to production, impacting how data flows and is utilized across platforms.

As one of the first hires, you will shape architecture, drive performance, and collaborate with a small founding team with deep AI and data expertise. This onsite SF role offers meaningful equity and rapid growth opportunities.

Qualifications

• Proficiency in Next.js and Python for building full-stack web apps.
• Experience with serverless/cloud functions for heavy data processing (GCP/AWS).
• Hands-on with Redis, Elasticsearch, and message queues to build scalable pipelines.

Responsibilities

• Build and scale data capturing workflows across web (Next.js) and desktop (Python).
• Architect and optimize high-throughput data pipelines with heavy loads.
• Collaborate with the founding team to define product strategy and ship features quickly.
• Set engineering best practices and lay groundwork for future team growth.

Skills

Full-stack development

Production deployment

Collaborative teams

Performance optimization

Tools

Next.js

Python

GCP

AWS

Redis

Elasticsearch

Kafka

RabbitMQ

Job description

About AfterQuery

AfterQuery is an applied research lab curating data solutions for foundation model development.

We serve every frontier AI lab with the mission of delivering the best data to power the best models. In doing so, we can make expertise that once took a lifetime to build available to anyone who needs it. Our customers are the ones building the foundation models themselves and our work sits directly in the loop of how those systems improve.

This is a rare opportunity to join a company at a defining moment in AI. Since raising our $30M Series A at a $300M valuation, AfterQuery has grown well over a $100M revenue run rate.

We're based in San Francisco and backed by leading investors including Altos Ventures, BoxGroup, and Y Combinator and angels from Google DeepMind, OpenAI, Anthropic, Meta Superintelligence Labs, and Microsoft AI.

Why Apply

Massive Opportunity:
We are one of the fastest-growing YC companies in our batch, and we believe we can become one of the fastest-growing YC companies of all time.

Founding Impact:
You will own and architect core infrastructure systems that power our platform from the ground up.

Equity & Growth:
Competitive salary and meaningful equity. As we scale, you'll have the opportunity to shape the engineering organization and lead major technical initiatives.

Strong Team:
Our founding team has experience from Citadel Securities, Meta, Google, Silver Lake, and Morgan Stanley - work alongside world-class engineers and researchers.

Overview

As one of our core engineers, you'll be critical in creating data infrastructure that will be shaping the future development of frontier models. You will be one of our first hires, and will get a lot of responsibility, owning entire projects end-to-end - meaning you can see your impact instantly.

Responsibilities

Build and scale complex data capturing workflows across web (Next.js) and desktop (Python)

Architect and optimize data pipelines that experience high throughput, with heavy loads

Collaborate with the founding team to define product strategy and ship features fast.

Set engineering best practices and lay the groundwork for future team growth.

Required Qualifications

Proficiency in JavaScript (Next.js) and Python, with demonstrated experience building full-stack web applications

Experience with serverless architecture and cloud functions for implementing heavy data processing (GCP/AWS)

Storage, caching & search: Hands-on with Redis (caching), Elasticsearch (indexing), and message queues (Kafka/RabbitMQ) to build scalable, low-latency pipelines.

Experience getting your ideas into production (founder experience preferred)

Preferred Qualifications

Background in AI research, LLM evaluations, or human-in-the-loop systems