Marketing Engineer
ProfoundNew York City$135k – $200kPosted 27 March 2026
Job Description
Marketing Engineer
Profound is on a mission to help companies understand and control their AI presence. We are hiring a Marketing Engineer to build the agents, automations, and data infrastructure that power Profound’s marketing engine.
You will sit at the intersection of marketing strategy and technical execution, designing and deploying the systems that allow the team to move faster and focus on higher-leverage work. This is not a traditional marketing role. You are not writing blog posts or manually running campaigns. You are building the engineering layer beneath it all, including autonomous agents, programmatic workflows, real-time data pipelines, and scalable infrastructure that turns marketing into a system.
You will be opinionated about architecture, relentless about data quality, and driven by the belief that marketing at scale is an engineering discipline. As we grow, you will define the systems and standards others build on. Initially, you are laying the foundation yourself.
WHAT YOU’LL DO
- Build marketing agents and automations to handle repetitive and time-sensitive workflows such as content drafting, social engagement, lead follow-up, sentiment monitoring, and campaign reporting
- Own the marketing data infrastructure by building the systems that connect campaign performance, AI citation tracking, attribution, and pipeline impact
- Create dashboards and reporting systems that keep marketing data accurate, actionable, and continuously up to date
- Build and maintain automation systems for search and AI visibility, including monitoring and optimizing how content performs across traditional search and AI-generated answers
- Implement structured data, schema markup, and metadata frameworks at scale
- Architect publishing and distribution systems by building integrations between CMS, data sources, and distribution channels
- Automate the publishing pipeline so content moves from creation to distribution with minimal manual intervention
- Instrument brand presence in LLMs by designing content architecture and structured data systems that increase visibility and citation frequency
- Run disciplined experiments across workflows, content structures, and distribution strategies, treating each deployment as a hypothesis
- Build repeatable systems by codifying successful workflows into templates, scripts, and scalable playbooks
WHO YOU ARE
- 4 or more years of experience in technical marketing, marketing engineering, growth engineering, or a similar role combining marketing strategy and technical execution
- Strong programming skills in Python, JavaScript, or similar, with experience building data pipelines, API integrations, and automation workflows
- Experience working with AI agents, LLM-powered tooling, or similar systems
- Comfortable operating in ambiguity and moving quickly in a fast-paced environment
- Familiarity with how search engines and AI models discover, index, and surface content, with experience in structured data such as schema.org http://schema.org or JSON-LD as a plus
- Systems thinker who prioritizes scalable solutions over one-off execution
- Clear communicator who can translate technical decisions into business impact
- Willing to challenge assumptions and test ideas with evidence rather than defaulting to best practices
- Experience with CMS platforms, headless architectures, publishing APIs, and marketing automation tools
- Experience in B2B or enterprise marketing environments is a plus
LOCATION
This is an on-site role based in our Union Square, NYC office, designed for builders who thrive on speed, iteration, and meaningful impact.
For this role, the expected base salary range is $135,000 to $200,000. Profound’s total compensation package is designed to be competitive and includes base salary, equity, and a full range of benefits and perks. Final compensation will depend on your experience and fit and will be determined during the interview process. Our recruiting team ... (truncated, view full listing at source)
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