{"id":1573599,"url":"https://alion.io/job/ai-fund-marketing-operations-engineer","title":"Marketing Operations Engineer","company":{"id":27001,"name":"AI Fund","domain":"aifund.ai","url":"https://alion.io/company/aifund","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":true,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":91,"open_postings":40,"ghost_share":0.075,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":88,"computed_at":"2026-10-15T05:46:00Z"}},"role":"Marketing","role_family":"Marketing","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mountain View, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":150000,"max":175000,"currency":"USD","period":"year","gross":null,"usd_annual":175000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Braze","optional":false},{"name":"Claude","optional":false},{"name":"GA4","optional":false},{"name":"Git","optional":false},{"name":"HubSpot","optional":false},{"name":"JavaScript","optional":false},{"name":"LLM","optional":false},{"name":"Marketo","optional":false},{"name":"Metabase","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false},{"name":"SQL","optional":false},{"name":"Stripe","optional":false},{"name":"YouTube","optional":false},{"name":"Amplitude","optional":true},{"name":"AppsFlyer SDK","optional":true},{"name":"Jira","optional":true},{"name":"Mixpanel","optional":true},{"name":"Semrush","optional":true},{"name":"Vercel","optional":true}],"status":"closed","first_seen_at":"2026-09-30T23:53:09Z","employer_posted_date":"2026-09-30","last_verified_at":"2026-10-14T01:36:56Z","board_verified":false,"closed_at":"2026-10-14T01:36:56Z","days_open":13,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":12},"description":"About The Role\nAI is the new electricity. Founded and led by Andrew Ng, DeepLearning.AI is on a mission to help everyone build with AI. Ten million people learn with us, and nearly all of them reach us through one system: HubSpot at the center, Customer.io sending on top of it, a warehouse and BI layer behind it, analytics feeding both, and a marketing site in front of it all.\nYou'll own that system - the architecture, the standards, and the numbers that come out of it. You design how the stack fits together, set the conventions everyone builds on, and direct the contractors who execute against them. You're not the person running a monthly hygiene cycle by hand; you're the person who defines what good looks like, builds the system that does it, and catches it when it doesn't.\nThis role exists because we believe the next unlock for our team isn't more headcount doing manual work - it's state-of-the-art agentic techniques doing the work with just a bit of human involvement. As a natural extension of the work, you'll often be the most credible source on our team for what AI tooling can actually do today, which gives us a built-in story for our own content.\nWhen someone asks \"is this number right\" or \"why isn't this attributing,\" you're who they ask. When someone asks how we define a new versus returning user, your answer is the answer, and it's written down.\nYou won't be writing copy, building sends, or running the publishing calendar. You'll be building the platform for the people who do.\nYou will report to the Head of Marketing and work in an office collaborating with some of the most forward-thinking AI engineers building the next generation of technology.\nWhat you'll do\nBuild the automation layer\nIdentify which marketing workflows are highest-leverage to automate first, in partnership with the Lifecycle Marketing Manager and Marketing Operations Coordinator.\n\nDesign and build agentic workflows against the systems you own: campaign QA, data validation, reconciliation, segmentation logic, and the reporting the team does by hand today. The weekly performance report should pull itself.\n\nBuild agents that check the system continuously rather than waiting for someone to notice - a sync that stopped, a property arriving null, two dashboards that stopped agreeing.\n\nDesign for verification, not just output. An agent reporting confidently on bad data is worse than no agent, so the evals ship with the workflow.\n\nBuild tooling that makes routine work self-service, so tactical requests go down over time rather than up.\n\nContinuously evaluate new agentic and AI tooling (Claude, other LLM APIs, automation platforms) and prototype how it applies to our specific funnel problems.\n\nOwn the stack\nOwn HubSpot as our system of record: object model, property architecture, workflows, permissions, reporting infrastructure, contact tiers and what they cost us.\n\nOwn the integration layer connecting HubSpot, Customer.io, the warehouse, PostHog, Stripe, and our mobile app, including the HubSpot-Customer.io seam. Which system holds truth for an attribute, how it gets there, what happens when they disagree. Write the middleware where native connectors fall short, and the monitoring that catches a sync failing silently.\n\nOwn the contact database and the schemas underneath it: property governance, lifecycle stages, dedupe logic, and the segmentation architecture campaign owners build on. Design events and properties that survive our next three launches.\n\nOwn deliverability infrastructure including authentication, domain reputation, bounce and complaint rates - instrumented so problems surface on their own.\n\nBe the liaison to engineering for the marketing site: know what marketing can change and what needs a developer, own the technical side of new page builds, and file the kind of ticket that gets picked up on the first read.\n\nOwn tracking and measurement\nOwn UTM conventions, tag deployment, and server-side conversion tracking, in partnership with engineering. Documented so that people follow them.\n\nInstrument new surfaces before they launch. When a product, page, or app build ships, tracking is live on day one because you scoped it during planning.\n\nBe the source of truth for marketing performance. Own the definitions, not just the dashboards including enrollments, memberships, revenue (MRR, ARR, churn, LTV), app installs and attribution, campaign performance, and write the queries behind them with Data Engineering where it goes deeper.\n\nFlag the caveats before someone builds a decision on them. A channel group with three months of history is not a trend, and you're the one who says so.\n\nOwn experimentation infrastructure\nBuild the infrastructure that lets the team test rigorously without engineering as a bottleneck: assignment, holdouts, sample sizing, and clean test structure so one variable moves at a time.\n\nSupport experimentation across lifecycle, acquisition, and paid. Give paid social a measurement foundation - audience and geography definitions, conversion tracking, and reporting that says what actually worked.\n\nDirect the work you don't do yourself\nScope, brief, and review contractor work. Write specs clear enough that execution doesn't come back wrong, and know the work well enough to catch it when it does.\n\nDefine recurring processes, automate what can be automated, and hand off the rest with the standard attached.\n\nDogfood & Content (Secondary)\nWhere 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.\n\nPartner with Developer Relations when a build is interesting enough to become a public case study or tutorial.\n\nWhat you should have\nAI-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.\n\n5+ years building or owning marketing systems - marketing engineering, marketing ops, growth engineering, or a software role embedded with a marketing team. Somewhere you owned the stack rather than just used it.\n\nReal engineering ability. Production JavaScript or Python, comfortable against REST APIs and webhooks, at home in Git. You've shipped things other people depend on.\n\nAn automation instinct. You see a recurring manual task as a system to be built, and you've built them. You use LLMs daily and have a real point of view on what they can and can't be trusted with.\n\nDeep experience administering a marketing automation or CRM platform - HubSpot, Customer.io, Marketo, SFMC, Braze, or comparable. If it isn't HubSpot, tell us how you'd get dangerous in it inside 60 days.\n\nComfort with data. You write and debug SQL against a warehouse, build in a BI tool like Metabase, and use GA4 without treating its numbers as gospel.\n\nReal understanding of tracking and attribution: UTM conventions, tag management, campaign structure, server-side conversion tracking, and where attribution commonly goes wrong.\n\nAn instinct for verification. You reconcile against a second source before publishing, and you notice when a dashboard has been quietly wrong for a month.\n\nA track record of running something end to end - a migration, a platform build, a consolidation - including the deprecation and the stakeholder work.\n\nA documentation habit. You leave definitions and conventions written down so the next person doesn't reverse-engineer them.\n\nOn-site reliability. Mountain View, five days a week.\n\nBonus if you have\nHands-on experience building with LLM APIs, agentic patterns, or eval frameworks\n\nExperience scoping and reviewing contractor or vendor technical work\n\nProduct analytics and event schema design (PostHog, Amplitude, Mixpanel)\n\nMobile attribution (AppsFlyer or comparable)\n\nStripe, Jira, Semrush, or tag management experience\n\nHeadless or publishing CMS platforms and deployment tooling like Vercel\n\nFast-paced startup or technical environment\n\nExperience writing technical content or documentation aimed at a developer audience\n\nFamiliarity with the AI/ML or technical education landscape\n\nWhat Success Looks Like\nIn 30 days you've mapped the stack, found where the numbers disagree, and built your first agent to help make things work, with a documented build that can double as content. In 90 days the definitions are documented, the weekly report pulls itself, and contractors are working against specs you wrote. In 6 months you own a function the team trusts by default, with enough automation that your time goes to the next problem instead of maintaining the last one.","description_format":"text","description_chars":8734,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Venture Capital","Angel Networks & Startup Accelerators"],"lifecycle":[{"event":"open","at":"2026-10-01T08:58:14Z"},{"event":"close","at":"2026-10-14T01:36:56Z"}],"visa":[],"liveness":null,"pay":{"stated_usd_annual":175000,"is_top_pay":true},"html_url":"https://alion.io/job/ai-fund-marketing-operations-engineer","json_url":"https://alion.io/job/ai-fund-marketing-operations-engineer.json","meta":{"generated_at":"2026-10-15T06:16:27Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":9405,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-16T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":27001},"rest":"https://alion.io/mcp/rest/get_company?id=27001"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fai-fund-marketing-operations-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fai-fund-marketing-operations-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fai-fund-marketing-operations-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/ai-fund-marketing-operations-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fai-fund-marketing-operations-engineer"}]}