Senior Software Engineer - Infrastructure

InCommon; San Francisco (CA); On-site; USD 180,000 - 280,000; Full time

Job summary

InCommon, an applied research lab in San Francisco, seeks a Senior Software Engineer - Infrastructure to design and build the core infrastructure powering data generation, evaluation, and AI workflows.

You will enable researchers and engineers to run large-scale human-in-the-loop workflows and data pipelines, ensuring scalability, reliability, and high throughput as the platform evolves with frontier AI development.

Qualifications

• Strong experience building production distributed systems
• 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

Responsibilities

• Design and build core infrastructure systems: Architect and develop the shared infrastructure powering the company's 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.

Job description

InCommon is hiring on behalf of an applied research lab.

About the company

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

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

The company is 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

The company is one of the fastest-growing YC companies in its batch, and believes it can become one of the fastest-growing YC companies of all time.

Founding Impact:

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

Equity & Growth:

Competitive salary and meaningful equity. As the company scales, you'll have the opportunity to shape the engineering organization and lead major technical initiatives.

Strong Team:

The 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 the company, you will design and build the core infrastructure that powers its data generation, evaluation, and agentic systems.

You will be responsible for the shared platforms that enable the company's 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 the company's 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
• 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 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