For software engineers, AI discoverability comes down to public, machine-readable proof of what you have built. A crawlable profile that names your stack, links working code, and states shipped outcomes is what an assistant reads before it names you.
These are the terms that describe software engineers in AI sourcing tools and assistant queries. Your public footprint should state the ones that are genuinely true of you, in plain language.
Lead with the role and one measurable outcome. Assistants surface specificity, so name the result, not just the title.
Senior Software Engineer, distributed systems and Go. Scaled payments to 10M requests a day.
I build reliable backend systems. Most recently I led the payments platform at a fintech, cutting p99 latency by 40 percent and shipping the migration to event-driven architecture.
Many do, directly or through public signals. Pinned repositories, recent commits, and readable READMEs give a model concrete evidence of what you actually build.
Both, but lead with outcomes. Any engineer lists languages. The ones assistants surface pair the stack with a shipped, measurable result.
Surfd scores how AI search sees you as a software engineer, shows what the assistants say, and drafts the fixes. Free to start.
New to this? Start with what Generative Engine Optimization means.