AI Engineer

Location: San Francisco, CA
Company Stage of Funding: Early-Stage AI Infrastructure Company
Office Type: Onsite
Salary: $200,000–$250,000 + Equity

Company Description

We’re representing an AI infrastructure company building one of the world’s most widely adopted AI gateways. Its platform provides a unified interface across more than 100 LLM providers, giving engineering teams a standardized way to manage model access, routing, authentication, budgets, observability, and governance.

The technology is used by thousands of engineering teams and trusted by organizations including Adobe, Netflix, and NASA. The company is now expanding beyond its core gateway product while continuing to build foundational infrastructure across LLMs, MCP, and AI agents.

This is an opportunity to join a small, highly technical team and work directly with the founders on infrastructure used by developers building production AI applications.

What You Will Do

• Build and maintain the interoperability layer that provides a unified interface across a large number of LLM APIs and providers.
• Develop transformations between OpenAI and Anthropic API specifications and the native formats of other model providers.
• Build provider-agnostic functionality across LLM APIs, including session management and /v1/responses functionality.
• Design gateway-level systems supporting provider capabilities such as skills, compaction, batches, and other emerging model features.
• Build developer infrastructure used by millions of users through the company's Python SDK.
• Work across LLMs, MCP, and AI agents, helping create a consistent developer experience across rapidly evolving ecosystems.
• Support a wide variety of MCP authentication flows and agent use cases.
• Work directly with the CEO and CTO on architecture, product, and engineering decisions.
• Engage directly with developers and customers to understand problems and translate feedback into product improvements.
• Ship quickly and take meaningful ownership over both implementation and product decisions.

Ideal Candidate Background

• 1–2+ years of professional backend or full-stack engineering experience building production systems.
• Strong Python experience.
• Experience building APIs and backend services, ideally using technologies such as FastAPI.
• Meaningful hands-on experience working with the OpenAI API.
• Understands the differences between /chat/completions and /responses and can discuss API-specific behavior and implementation nuances.
• Strong understanding of modern LLM APIs and how developers integrate models into production applications.
• Comfortable learning the APIs and behavior of different model providers and translating between them.
• Strong engineering fundamentals around API design, reliability, and production software.
• Comfortable operating with significant ownership on a small engineering team.
• Interested in speaking directly with users and solving real developer problems.
• High level of curiosity and interest in the rapidly evolving AI infrastructure ecosystem.

Preferred

• Experience building LLM infrastructure, AI gateways, or developer tooling.
• Experience working across multiple LLM providers rather than exclusively with a single model API.
• Familiarity with Anthropic's API in addition to OpenAI.
• Experience with MCP and emerging agent infrastructure.
• Experience building or contributing to open-source software.
• Experience maintaining SDKs, APIs, or developer-facing infrastructure with significant external usage.
• Familiarity with Redis and PostgreSQL.
• Experience thinking deeply about developer experience and API ergonomics.
• Comfortable working directly with founders in a fast-moving early-stage environment.

Compensation and Benefits

• Base salary: $200,000–$250,000.
• Equity included.
• Health, dental, and vision benefits.
• Location: San Francisco, CA.
• Office: Onsite.
• Core technology stack includes Python, FastAPI, Redis, and PostgreSQL.
• Opportunity to work directly with the CEO and CTO on critical product and infrastructure decisions.
• Significant ownership over products used by thousands of engineering teams.
• Best suited for an engineer excited about LLM APIs, interoperability, developer infrastructure, and open-source software, rather than someone primarily interested in model research or training.