GTM Engineer

WorkOS•San Francisco, CA

5d•Remote

About The Position

WorkOS builds modern developer tools and APIs that make it easy for companies to become Enterprise Ready. Our platform powers authentication, identity, authorization, and other critical infrastructure that developers need to securely scale their products to large organizations. We recently raised a $100M Series C, valuing the company at $2B, led by Meritech and Sapphire with participation from Greenoaks, Craft, Abstract, and Audacious. WorkOS powers enterprise features for many of the fastest-growing AI companies, including OpenAI, Cursor, and Perplexity, Vercel, and Plaid. As AI reshapes software, WorkOS is at the frontier of Human and Agent Authentication, Identity, and Access Control—helping companies answer a new critical question: who are your agents, and what are they allowed to do? Our fast-growing customer base includes hundreds of modern software companies building the next generation of enterprise-ready products. We're looking for a GTM Engineer to join our team. This is a hands-on engineering role embedded in our GTM org. You'll own the systems that power how we identify, enrich, score, and engage prospects at scale: a custom built enrichment pipelines leveraging Cargo, the campaign sync tooling that feeds our events and outbound plays, and the code that ties it all together. You'll work at the intersection of GTM strategy and software engineering by writing production TypeScript/Python, shipping PRs, designing schemas, and building internal tools that the whole GTM team depends on daily. Think of it as being the engineer who makes the rest of the GTM team unreasonably effective.

Requirements

Capable software engineer. You write clean TypeScript or Python, understand async patterns, know how to design a database schema, and can debug a production incident from logs alone.
Experience with GTM tools: Salesforce, HubSpot, Clay, Cargo, Apollo, or equivalents.
Understanding of how leads flow through a CRM, what enrichment providers actually return, and why data quality degrades over time.
Systems thinking: You think in pipelines, not one-off scripts. When you build something, you think about what happens at 10x volume, when an API goes down, or when the same company gets submitted five times simultaneously.
3+ years of experience in GTM Engineering, Revenue Operations, or a closely adjacent technical role at a high-growth B2B SaaS company.

Nice To Haves

Ideally you've had a BDR or AE background that gives you intuition for what the sales team actually needs.
You don't need to have done this exact job before. You need to care about the craft.
The best GTM Engineers at WorkOS are the ones who spot a broken workflow in Slack at 11pm, open a PR the next morning, and ship the fix before the standup.
They're the ones who, when the sales team complains about bad data, don't just route it to RevOps, they dig into the Snowflake schema and find the upstream issue.

Responsibilities

Maintain and improve our core enrichment pipeline: the workflows that take a new signup or a conference attendee list and return a fully enriched, scored, and Salesforce-upserted record.
Work on the fit scoring pipeline: the logic that evaluates every company flowing through our stack against our ICP and writes results to Snowflake and SFDC.
Build and maintain the systems that sync event attendee lists (Luma, conference CSVs) through enrichment and into Salesforce campaigns.
Build and maintain enrichment tooling accessible via Wallaby, WorkOS's internal AI agent that the GTM team uses to look up prospects, run ad-hoc enrichments, and surface account intel on demand.
Work closely with the data engineering team to keep our Snowflake schema in sync with the tools that consume it, track downstream pipeline impacts when schemas change, and ensure the data flowing into SFDC is accurate and up-to-date.
Ship real code. PRs go through review. Debug production incidents (Salesforce API rate limits, Cargo timeouts, enrichment failures). Instrument logging, add observability, and think carefully about batching, concurrency, and idempotency.