Senior Software Engineer - Infrastructure

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 a Senior Software Engineer - Infrastructure at AfterQuery, you will design and build the core infrastructure that powers our data generation, evaluation, and agentic systems.

You will be responsible for the shared platforms that enable our engineers and research teams to run large-scale human-in-the-loop workflows, evaluation harnesses, and automated data pipelines used to train frontier AI models.

This is a highly technical role with broad ownership. You will architect and build foundational infrastructure that many other engineers depend on, ensuring systems are scalable, reliable, and capable of supporting extremely high-throughput workloads.

You will work directly with the founding team to define system architecture, establish engineering best practices, and build the infrastructure that supports the next generation of AI development.

Responsibilities

Design and build core infrastructure systems

Architect and develop the shared infrastructure powering our data generation platforms, human-in-the-loop systems, and evaluation pipelines.

Develop scalable distributed systems

Build systems capable of processing large-scale datasets and high-throughput workloads with strong reliability guarantees.

Build internal platforms and shared services

Create reusable infrastructure and APIs that enable product engineers and researchers to build quickly and reliably on top of core systems.

Optimize performance and reliability

Design systems with strong observability, monitoring, and fault tolerance to support production workloads at scale.

Own infrastructure architecture

Help define long-term system architecture across data pipelines, compute infrastructure, task orchestration, and storage systems.

Collaborate across engineering and research

Work closely with engineers and researchers to support new AI experimentation workflows and platform capabilities.

Establish engineering best practices

Define standards for system design, deployment, reliability, and infrastructure operations.

Required Qualifications

Strong experience building production distributed systems or platform infrastructure

Proficiency in Python and/or JavaScript (Node.js / Next.js) or similar backend technologies

Experience designing and operating systems in cloud environments (GCP or AWS)

Experience with message queues and event-driven systems (Kafka, RabbitMQ, Pub/Sub, etc.)

Experience working with high-throughput data pipelines and asynchronous processing systems

Strong understanding of system scalability, performance, and reliability

Experience owning systems running in production environments

Preferred Qualifications

Experience building internal developer platforms or shared infrastructure

Experience supporting large-scale data processing pipelines

Experience with AI infrastructure, LLM evaluation systems, or ML pipelines

Experience working at high-growth startups or scaling early infrastructure

Experience designing human-in-the-loop or workflow orchestration systems