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AI Engineer, Marketing

AI Summary
Instructure is seeking an AI Engineer to build and operate production AI tools that improve workflows across marketing functions. The role combines LLM prompt development, API and webhook integrations, automation, basic Python scripting, and ongoing monitoring of deployed tools. The engineer will work with platforms such as Salesforce, Marketo, Asana, CMS tools, Claude, Gemini, Clay, and Glean. Responsibilities also include documentation, user enablement, issue triage, data and brand-quality guardrails, and measuring tool adoption and ROI. This is a hands-on remote US role for an adaptable builder who can move prototypes into dependable internal products.
Remote from
USA
Salary
USD 85k–90k / yr
Employment
Full Time
Experience
Senior
Published
Apply before
5 Dec 2026
Listing views
29
Application actions
2
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Opportunity details

About this role.

At Instructure, we believe in the power of people to grow and succeed throughout their lives. Our goal is to amplify that power by creating intuitive products that simplify learning and personal development, facilitate meaningful relationships, and inspire people to go further in their education and careers.
We do this by giving smart, creative, passionate people opportunities to create awesome. And that’s where you come in:

Why This Role Exists

Marketing’s AI tools are already in production and delivering real results – localization time down 90%, meeting prep down 90%, case study builds down 70%. Demand for new tools now outpaces what one person can build. This role adds dedicated engineering capacity so the team can ship more, support what’s live, and keep raising the bar on quality without becoming a bottleneck.

Scope

You’ll build for all marketing functions – Brand, Content, Events, PMM, International, and SDR. If a marketing team has a workflow AI can improve, it’s in scope. You’ll partner closely with the Senior Manager on where to focus, then own your pieces of the build.

Day to day, you’ll work across the marketing tech stack (Salesforce, Asana, Marketo, CMS platforms) and the automation and LLM tooling that connects them.


Key Responsibilities

AI Tool Development (~50%)

  • Build and maintain production AI tools for marketing teams – content generators, meeting and SDR prep systems, case study automation, and workflow integrations.

  • Write, test, and iterate prompts across LLM-powered applications; version them and track what works.

  • Build integrations between AI tools and the marketing tech stack (Salesforce, Asana, Marketo, CMS platforms) using APIs, webhooks, and automation platforms.

  • Take tools from prototype to production with clear quality checks, and own them after launch – monitor outputs, tune prompts, fix drift, and ship improvements based on user feedback.

Enablement & Documentation (~25%)

  • Write clear, usable documentation and playbooks so teams can self-serve.

  • Help train marketing teams on the tools you build – not just the clicks, but how to get good output and when AI isn’t the right fit.

  • Support the AI Incubator intake process: help evaluate submissions and move projects from sandbox toward production.

Quality & Support (~25%)

  • Keep production tools inside data and brand guardrails; flag anything that touches customer data or brand voice for review before it ships.

  • Respond to user issues on live tools, triage what’s breaking, and close the loop.

  • Help track tool usage and impact so every tool has a defensible ROI story.

Required Qualifications

  • Proficiency with or ability to quickly ramp on our primary AI and marketing tech stack, including Claude, Gemini, Clay, and Glean.

  • 2+ years in marketing technology, automation, software, or a comparable technical role or equivalent hands-on project experience.

  • Hands-on experience with LLMs: writing and iterating prompts, evaluating output quality, and working within context limits.

  • Comfort with technical integration: APIs, webhooks, automation platforms (Zapier/Make), and at least basic scripting (Python preferred).

  • Strong written communication – this role produces documentation others rely on.

  • Comfort with ambiguity and a bias toward shipping. This function is evolving, and you’ll iterate constantly.


Preferred Qualifications

  • Experience in B2B SaaS marketing, ideally EdTech or enterprise software.

  • Familiarity with marketing ops tooling: Salesforce, Marketo, Asana, Google Analytics, or comparable platforms.

  • Experience building something real with AI that people actually used – a tool, a bot, an automation, even a side project counts.

  • Exposure to CMS or web platforms (Drupal, content management, localization workflows).

  • Experience with GitHub and Railway for version control and deployment

Get in on all the awesome at Instructure!

We offer competitive, meaningful benefits in every country where we operate. While they vary by location, here’s a general idea of what you can expect:

  • Competitive compensation, plus all full-time employees participate in our ownership program – because everyone should have a stake in our success.

  • Flexible work culture. Our remote, hybrid and in-office collaboration spaces vary by role, team and location.

  • Generous time off, including local holidays and our annual “Dim the Lights” period in late December, when teams are encouraged to step back and recharge based on departmental needs.

  • Comprehensive wellness programs and mental health support

  • Learning and development resources, including professional development tools and tuition reimbursement, to support your growth

  • The technology and tools you need to do your best work

  • Motivosity employee recognition program

  • A culture rooted in inclusivity, support, and meaningful connection

We believe in hiring great people and treating them right. The more diverse we are, the better our ideas and outcomes.

Instructure is an Equal Opportunity Employer. We comply with applicable employment and anti-discrimination laws in every country where we operate.

All employees must pass a background check as part of the hiring process. To help protect our teams and systems, we’ve implemented identity verification measures. Candidates may be asked to verify their legal name, current physical location, and provide a valid contact number and residential address, in accordance with local data privacy laws.

Any attempt to misrepresent personal or professional information will result in disqualification.

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

4/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

4/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThe role requires practical LLM engineering, integrations across several marketing systems, and ownership of production quality after release. Requirements are evolving, so success depends on independently prioritizing ambiguous work while communicating clearly with nontechnical marketing users.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$87,500
US market range (per year)$85k–$120k
AI insightThe employer discloses a yearly base-pay range of $85,000–$90,000 USD, with a midpoint of $87,500. This is a plausible but relatively modest range for a US AI engineering role; the estimated US market range for a hands-on marketing AI engineer is approximately $85,000–$120,000 per year, depending on location, scope, and production engineering depth.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you take an LLM-based marketing workflow from prototype to production?

I would first define the user, workflow, success metrics, and data boundaries. Then I would build a narrow prototype, create representative test cases and quality criteria, integrate it with the required systems, and release it to a small user group. After launch, I would monitor output quality, usage, failures, and feedback, then version prompts and iterate against measurable results.

How do you evaluate and improve prompt quality for a marketing AI tool?

I create a test set that reflects common, edge-case, and high-risk requests, then score outputs for accuracy, brand alignment, completeness, and safety. I make prompt changes one at a time, compare results against a baseline, and retain version history. For sensitive workflows, I add structured inputs, output constraints, human review checkpoints, and escalation rules.

Describe how you would integrate an AI tool with Salesforce or Marketo.

I would map the business event and the minimum fields needed, verify access controls and consent requirements, and use the platform API or an automation layer to retrieve and write data. I would validate payloads, implement retries and error logging, avoid exposing unnecessary customer data to the model, and document ownership and support procedures for the integration.

What would you do if users report that a previously reliable AI tool is producing lower-quality outputs?

I would triage the issue by checking recent prompt, model, source-data, integration, and workflow changes. I would reproduce the problem using logged examples where permitted, compare output against prior test cases, and determine whether the cause is prompt drift, data quality, model behavior, or an integration failure. I would deploy a tested fix, communicate the status to users, and add monitoring or regression tests to prevent recurrence.

How would you demonstrate ROI for an internal AI automation?

I would establish a baseline before launch, such as time per task, turnaround time, error rate, throughput, or external spend. After adoption, I would track usage, completion rates, quality outcomes, and time saved while collecting qualitative feedback from users. I would present the results alongside operating costs and limitations so stakeholders can make an informed decision about scaling the tool.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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