Staff Software Engineer, Enterprise Platform

GitHub is looking for a Staff Software Engineer to join the Enterprise Platform team, focussed on building AI Governance, Policy Controls and Observability for Github and Copilot. You will build the platforms, services, and controls that help enterprise customers securely configure, govern, and operate GitHub Copilot at scale.  

This is a hands-on platform engineering role centered on cross-team integration, workflows, and state management. You will turn customer and business requirements into clear APIs, coherent domain models, enforceable policies, and reliable production services. The work spans questions such as which service owns a configuration change, how that change moves through dependent systems, when it takes effect, and what evidence administrators need to understand the result.

You will design reliable behavior across service boundaries: clear ownership of state, explicit workflow transitions, safe retries, handling of partial failures, and compatible API and event contracts. Success means more than choosing an architecture. It means explaining the tradeoffs, implementing the critical paths, agreeing on integration contracts with partner teams, and owning the system in production.  

Deep production engineering experience is essential. Prior AI or LLM experience is valuable but not required. We welcome engineers who have personally designed, implemented, and operated services that coordinate workflows and state across multiple systems. We value clear reasoning about service contracts, data ownership, failure behavior, and safe evolution. Experience building an AI integration is useful when accompanied by evidence of correctness, evaluation, and operational ownership.  

You will partner with product, design, security, and other Copilot engineering teams to make governance capabilities trustworthy and straightforward to adopt across various client surfaces. We value curiosity, maintainable systems, clear communication, and collaboration. How we build is as important as what we ship. 

• Design, implement, test, ship, and support backend services and APIs for enterprise configuration, policy enforcement, and auditing.

• Develop clear domain models and API and event contracts, including state ownership, resource lifecycles, authorization boundaries, and compatibility.

• Implement workflows across dependent services, with explicit state transitions, idempotent operations, and recovery from interrupted or partially completed work.

• Lead architecture decisions by framing the customer problem, identifying constraints, evaluating alternatives, and making correctness, reliability, cost, and usability tradeoffs explicit.

• Remain close to implementation through coding, debugging, prototyping, pairing, and code review. Help other engineers build extensible, maintainable software.

• Partner with teams that own upstream and downstream services to agree on integration contracts, dependency behavior, and ownership. Design versioning, staged rollouts, and migrations that consumers can adopt safely.  

• Own service health through meaningful telemetry, actionable alerts, incident response, and participation in on-call and first-responder rotations.  

• Establish success criteria before launch. Use automated tests, production telemetry, customer feedback, and, where relevant, AI evaluations to identify failures and measure improvement.  

• Build and improve CI/CD pipelines and release practices that support safe, repeatable delivery.  

• Write architecture briefs and proposals that make complex decisions understandable and support alignment across teams.  

• Drive a technical roadmap aligned with product goals, balancing customer capabilities with reliability, security, maintainability, and engineering cost. 

Preferred Qualifications:  

• Experience building shared enterprise capabilities such as policy enforcement, configuration management, or auditing, with ownership of implementation and production operation. 

• Experience with asynchronous workflows, event-driven integrations, or reconciliation between systems with different data models or lifecycle semantics.  

• Experience building AI-assisted workflows or agent-based systems, including tool integration, evaluations, failure analysis, and quality, latency, and cost tradeoffs.  

• Experience with Go, Ruby, or similar general-purpose programming languages.  

• Experience partnering closely with product management and design on administrator-facing or developer-facing products.