Applied AI Engineer

About Snowplow Snowplow is the real-time customer context layer that collects, validates, enriches, and delivers behavioral data for advanced analytics, ML, and AI agent decisioning. Snowplow's event tracking is leveraged across 2M+ websites and applications globally, processing over one trillion events per month.

More than 250 companies, including Experian, Auto Trader, Strava, Condé Nast, and Hello Fresh, rely on Snowplow to build a well-governed, first-party data foundation that powers their customer-facing AI agents, in-session personalization and recommendations, and real-time analytics.

This Opportunity Snowplow is rebuilding how the company runs its own work, and the Applied AI team owns Firn, the data and AI platform that work runs on, and the engineering standards that go with it. This role is an Applied AI engineer embedded with one business team, the way a site reliability engineer sits inside a product team: your line manager, your code reviewers and your career path come from Applied AI;

your backlog and your day come from the team you serve. The Senior Director you report to still ships code.

Your team is Growth, the group inside Marketing that books new meetings for our sellers. Its workflows already produce pipeline: an always-on outbound engine that scores accounts daily and sends hundreds of personalized sequences, a campaign builder that turns an AE's target list into a reviewed campaign, a morning routine that ranks warm contacts by intent signal, enrichment and signal pipelines from Apollo, Vector and reo.dev

into Snowflake, and a paid-media loop that adjusts ads. They were built by people close to the work to get results fast, and they did. Now they need an engineer who makes them production grade and keeps them improving.

You take over that estate, make it reliable, and make it better every week. The Director of Growth sets what matters, which is what moves pipeline now. You decide how it gets built and you build it. You work with our data engineers on scoring models and the semantic layer, with the platform engineer on the framework your workflows move onto, and with the Growth Associates and AEs who use what you ship.

The engineering standards are Applied AI's; the tempo is Growth's.What you will ownThe GTM workflows in production. The outbound orchestrator and its sequences, the campaign builder, the intent-signal routine, the enrichment and signal pipelines, and the paid-media loop. You know how each one runs, what it costs, and what to do when it breaks.

Review steps before anything sends. Agents that check the list, confirm each signal is current, and review the copy before a sequence goes out, with sampled human sign-off.

The learning loop. Replies and meetings booked feed back into who we contact and what we say. You instrument the sends, run A/B tests on variants, and show the Growth team which messaging is working.

Signals and enrichment. The pipelines that bring third-party signals and first-party behavior into Snowflake, the fields the Growth team needs exposed through our MCP tools, and the work with our data engineers that gets them there.

Skills and routines for sellers. The Claude skills the Growth Associates and AEs run each morning, the campaign tooling, and the short working sessions that get a new team using them.

Deliverability and suppression. One set of contact rules every sender reads, warm-inbox discipline, and the suppression windows that keep us welcome in people's inboxes.

Engineering standards on your estate. Version control, alerting, tracing, runbooks and documented handover for every workflow you run, and a steady move of those workflows onto the Firn factory framework.

The weekly cadence. A standing backlog with the Director of Growth,…