Principal Software Engineer, Full Stack — Codex

OpenAI•San Francisco, CA

2d•$459,000 - $536,000

About The Position

OpenAI is hiring a Principal Software Engineer to define and build the next generation of AI-powered software development workflows for Codex. The role spans user interfaces, developer tooling, workflow orchestration, agent and prompt design, backend systems, and cloud infrastructure. It is a product-oriented technical leadership position focused on identifying high-leverage opportunities across the software development lifecycle and turning ambiguous problems into durable product and system strategies. The successful candidate will lead major initiatives across engineers and partner teams while remaining a hands-on senior individual contributor. The work will shape Codex’s architecture and how developers design, write, review, test, and maintain software. The role includes setting technical and product direction, making high-consequence architectural decisions, developing reliable and safe AI-powered workflows, and working with users and cross-functional partners from exploration through launch and iteration. OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. It emphasizes safe deployment, human needs, and the value of diverse perspectives and experiences. OpenAI is an equal opportunity employer and provides reasonable accommodations to applicants with disabilities.

Requirements

Experience leading the design and delivery of technically complex, user-facing systems with impact beyond a single feature or team.
Deep software-engineering expertise and strong product judgment across product interfaces, distributed systems, infrastructure, and developer tooling.
Track record of creating clarity in ambiguous problem spaces, setting technical direction, and bringing multiple teams or disciplines along.
Ability to remain hands-on while increasing other engineers’ effectiveness through architecture, mentorship, decision-making, and technical leadership.
Deep understanding of modern software-development workflows and the ability to recognize where AI agents require new interaction and system designs.
Experience building or operating AI-powered products, with understanding of nondeterminism, evaluation, latency, reliability, safety, and rapidly evolving model capabilities.
Ability to make thoughtful tradeoffs among speed, product learning, technical quality, and long-term leverage.
Ability to develop perspectives from user evidence and revise them as technology and user behavior evolve.
Clear communication across engineering, product, design, research, and executive audiences, particularly amid uncertainty or organizational complexity.
Motivation to define problems as well as solve them, and to create technical and organizational conditions for others to deliver excellent work.

Responsibilities

Set the technical and product direction for significant areas of Codex, translating product vision into scalable architectures and actionable engineering strategy.
Lead complex, multi-quarter initiatives spanning client applications, backend services, agent systems, cloud infrastructure, and developer tooling.
Design AI-powered workflows that generalize across languages, codebases, development environments, and organizations while remaining intuitive, reliable, and safe.
Make high-consequence architectural decisions under ambiguity, balancing rapid product learning with long-term system quality and adaptability.
Identify opportunities across team or product boundaries, align stakeholders, and drive work from exploration through launch and iteration.
Partner with product, design, research, infrastructure, security, and go-to-market teams to turn model capabilities and developer needs into coherent product experiences.
Work directly with developers, open-source maintainers, startups, and large enterprises to uncover unmet needs and identify foundational opportunities.
Establish patterns and standards for reliability, observability, performance, experimentation, and evaluation across AI-powered developer workflows.
Review critical designs, mentor engineers, improve engineering practices, and help the team navigate difficult product and technical decisions.
Contribute directly to implementation, particularly on novel or technically demanding problems that connect multiple product layers.