Marketing Engineer

Salary Range: 150000 to 175000 (Currency: USD) (Pay period: per-year-salary)

What You Will Do

Automation & Agentic Tooling

Design and build agentic workflows that automate lifecycle triggers, campaign QA, segmentation logic, and reporting that the team currently does by hand

Identify which marketing workflows are highest-leverage to automate first, in partnership with the Lifecycle Marketing Manager and Marketing Operations Coordinator

Build internal tools that let non-technical marketers configure and launch automations without engineering support

Continuously evaluate new agentic and AI tooling (Claude, other LLM APIs, automation platforms) and prototype how it applies to our specific funnel problems

MarTech Integration

Own the technical integration layer between Customer.io, Stripe, PostHog, our platform's event data and any other tools we might use, so lifecycle triggers fire on accurate, real-time data

Support various platform and data migrations with scripting, platform expansions, and workflow recreation as needed

Build and maintain the data pipelines that feed our Metabase dashboard, partnering with Data Engineering to close our current LTV and cohort data gaps

Set up and maintain UTM, tracking, and attribution infrastructure so channel performance data is trustworthy

Experimentation Infrastructure

Build the technical infrastructure for A/B testing across lifecycle, email, and on-platform messaging, so the Lifecycle Marketing Manager and PMM team can test rigorously without engineering as a bottleneck

Build self-serve reporting tools that let marketing stakeholders answer their own data questions without filing a ticket

Dogfood & Content (Secondary)

Where it's a natural byproduct of the automation work, document exciting or interesting builds for use in our content across our newsletters, YouTube accounts, events, or technical blogs. Note this supports the content engine but is not a primary deliverable or goal.

Partner with Developer Relations when a build is interesting enough to become a public case study or tutorial

What You Bring

Required

AI-native, default to using AI-assisted coding and building automations in everything you do. Appetite, passion for and proven record of learning and experimenting with the newest AI engineering best practices. 

A minimum of 3 years experience as a software engineer, with at least 1–2 years applying that experience to marketing, growth, or RevOps problems

Hands-on experience building with LLM APIs (Claude, OpenAI, or similar) and agentic workflows or tool-use patterns

Strong scripting and integration skills (Python or JavaScript/Node) and comfort working with REST APIs and webhooks

Experience integrating or building on top of a CRM or marketing automation platform (Customer.io, HubSpot, Braze, or similar)

Working knowledge of SQL and comfort building or maintaining data pipelines

Experience with event tracking and analytics tools (PostHog, Amplitude, Segment, or similar)

Ability to translate a marketer's manual, repetitive workflow into a clear technical specification, and ship it without heavy oversight

Nice to Have

Experience with Stripe or other subscription billing APIs

Familiarity with Customer.io, Hootsuite, or Metabase specifically

Experience writing technical content or documentation aimed at a developer audience

Background in growth engineering, marketing ops engineering, or a similar hybrid role

Familiarity with the AI/ML or technical education landscape

What Success Looks Like

In your first 30 days, you will have mapped our current martech stack and identified the highest-leverage manual workflow to automate first, plus you will have shipped your first automation into production, with a measurable reduction in manual work for the team and a documented build that can double as content. In 6 months, you will have built a durable automation layer across multiple marketing functions, established yourself as the team's go-to technical partner, and helped the team operate well beyond what its headcount alone would suggest is possible.
 
At DeepLearning.AI, we are committed to fostering a workplace of mutual respect and equal opportunity. We hire based on qualifications, merit, and business needs, without discrimination on any characteristic - whether protected by applicable laws or not. Our goal is to attract, recruit, develop, and retain the best talent from a diverse candidate pool.