GitHub is shaping the future of software development in the age of AI agents. The difference between a general-purpose agent and an expert is often whether it can find, understand, and apply the right context at the right time. One of our goals is to make agents more effective and efficient by default, without requiring every developer or team to become an expert in configuring AI systems. We're looking for a Staff Software Engineer to help ideate, develop, experiment, and build GitHub’s context layer that is part of our agent platform – this includes agent memory, search, GitHub graph, bringing the right context to agents efficiently and at the right time. This work will help agents use the context available across code, issues, pull requests, workflows, connected developer tools, and much more. It will also contribute to agentic experiences that can learn from prior work, make higher-quality decisions, and carry useful context across tasks and product surfaces. You'll be part of our Copilot Agents organization, working with a strong group of engineers and partnering across product, engineering, design, research, and data science. Your work can positively impact millions of developers and the AI agents helping them build everything from open-source projects to global enterprises. We are looking for creative problem solvers and diverse thinkers, people who care about culture as well as customers and features. We believe that how we do things is as important as what we do. Big vision, a common purpose, passion for quality, moving fast and learning quick. Great products reflect the teams that build them. The ideal candidate has deep experience designing and building agent harnesses, evals, developer tools, context retrieval, and has worked on one or more of information retrieval, knowledge or context systems, developer platforms, or production generative-AI applications. You can move from ambiguous product problems to clear technical direction, build end-to-end systems, and help multiple teams make coherent architectural choices. You learn quickly, communicate clearly, and combine experimentation with operational rigor.
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Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed