For ML and LLM engineers, joining a startup as a founding engineer can be one of the highest-leverage moves of their career and also one of the easiest opportunities to misunderstand. We spend years learning distributed systems, model serving, evals, RAG, MLOps, and how to make GPUs do expensive things slightly faster. Then someone offers us 0.5% equity and suddenly we’re expected to understand dilution, strike prices, vesting, cliffs, preferred shares, liquidation preferences, runway, SAFEs, and whether “we’re raising our Series A soon” is good news. This talk is about that transition. I’ll break down the financial and startup concepts engineers should understand before accepting a founding-engineer role, how to reason about equity versus salary and risk, and the questions worth asking founders before joining. I’ll also cover what the job actually requires: moving beyond being a strong ML engineer into someone who can operate with ambiguity, talk to customers, make uncomfortable technical tradeoffs, understand the business, ship with incomplete information, and help build both the product and the engineering culture around it. Finally, I’ll make the case for why the current AI cycle creates an unusual window for ML and LLM engineers to become founding team members and how to position yourself to capture the upside without treating every startup offer like a lottery ticket.
Sebastian Gomez is a staff-level engineer and former startup CTO who has spent 11 years building production systems, including the occasional system that probably should have started as a spreadsheet. He co-founded an AI insurtech, has built ML, LLM, data, and enterprise platforms from zero to production, and now works on production computer-vision infrastructure. Having sat on both sides of the founding-engineer conversation, he is particularly interested in helping engineers understand what they are actually signing up for.
