Applied AI Engineer

Snowplow Analytics Limited; Massachusetts; Hybrid; USD 130,000 - 180,000; Full time

Benefits offered by this job

Meaningful equity stake

Flexible working

Generous PTO

Medical, Dental & Vision Insurance

401k

MacBook and home office equipment

Enhanced Family leave

Job summary

Snowplow Analytics Limited is seeking an Applied AI engineer embedded with the Growth team to build and scale production-grade data and AI workflows. You will own the GTM outbound engine, campaign tooling, ranking signals, and enrichment pipelines, ensuring reliability and measurable impact on pipeline and conversions.

You will collaborate with data engineers, platform engineers, and Growth Associates, applying AI-native practices and robust alerting to keep systems resilient and resilient.

Qualifications

• Production-grade software written in Python/SQL.
• Experience using LLMs for real tasks and evaluation.
• Reliability: alerting, retries and error handling.
• Data pipelines into Snowflake or equivalent.

Responsibilities

• Own GTM workflows in production: outbound sequences, campaign builder, signals, enrichment and paid-media loop.
• Review steps before sending: signal validity and copy review with human sign-off.
• Build learning loop: instrument sends, run A/B tests, inform Growth priorities.
• Manage signals and enrichment pipelines and MCP tool exposure.
• Create and maintain inventory of workflows with engineering standards and handover docs.
• Maintain deliverability and suppression rules across campaigns.
• Establish a weekly cadence with Growth to align on shipped work and impact.
• Demonstrate AI-native workflows during interview.

Skills

Python

SQL

Git

APIs

LLMs

Tools

n8n

Clay

Zapier

Snowflake

dbt

Job description

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, AutoTrader, Strava, Condé Nast, and HelloFresh, 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 own

• The 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, priorities set by pipeline, and a short written account of what shipped and what it did. When a priority and an engineering standard conflict, the Director of Growth and the Senior Director of Applied AI settle it together.

We would love to hear from you if you have

• Written software that runs in production. Python and SQL are your working languages, you use git every day, and when you meet a new API you read the docs and use it. Tools like n8n, Clay or Zapier are in your kit, and when they run out you write the code.
• Built with LLMs for real work. Prompts, tools, structured outputs and evaluation, and the failure modes of each. You know what a model does when a prompt does not say where to get its data, and you design against it.
• Run something that could not go down. Alerting, error handling, retries and recovery are habits, and when something does fail you fix the class of problem, not the instance.
• Moved data into a warehouse. You have built or maintained pipelines into Snowflake or an equivalent, and you can model a table in dbt or learn to quickly.
• Sat with sellers and marketers. You can take a growth director's ask, work out what they need, and come back with something they use the next week. When an ask is a symptom of a broken process, you say so before you build it.
• A fast tempo. The team you serve measures in meetings booked per week. You are measured on what you build: whether it runs, what it costs, and what it changed for the team. You ship small, measure, and say plainly when something is not working.
• An AI-native way of working you can show us. A workflow, automation or agent you built and still use. Expect to demonstrate it during the interview process.
• Comfort being embedded. You can sit inside another team, take its priorities, and still hold the engineering standards your own team sets.

Bonus points for

• Experience with Salesforce, Apollo, Smartlead, Lemlist or similar GTM tooling and their APIs.
• Experience with email deliverability at volume.
• Experience with paid-media platform APIs.

How we hire for this role

The interview process includes a short technical exercise in code and a working session where you walk us through how you work with AI on a real task, using your own setup. We work this way ourselves, and you are welcome to use AI when applying.

What you get in return for being awesome

• Meaningful equity stake
• Competitive Base Salary
• Flexible working
• Generous PTO
• Medical, Dental & Vision Insurance
• 401k
• MacBook and home office equipment allowance
• Enhanced Family leave