Embedded Product Manager, AI Marketing Ops

Code and Theory, LLC; Austin, New York (TX, NY); On-site; USD 70,000 - 140,000; Full time

Job summary

Code and Theory seeks an Forward Deployed Product Manager to be embedded in implementation pods, translating client and internal needs into clear product requirements during platform rollouts. You will bridge product, delivery, and implementation teams, defining workflows, prototyping solutions, and shaping how The Machine's agents behave in real-world use.

You will own end-to-end workstreams, moving between client context, product thinking, and delivery realities, and you will create structure

Qualifications

• Experience leading product efforts in startups, SaaS, or technical services environments.
• Ability to own workstreams end-to-end with limited supervision.
• Strong systems thinking and workflow design.
• Hands-on with prototyping, prompting, and AI-enabled workflows.
• Excellent communication across technical and non-technical stakeholders.
• Bias to action and willingness to define playbooks.

Responsibilities

• Gather and clarify business requirements from clients and internal teams during implementations.
• Translate workflows into structured requirements, user stories, and briefs for delivery teams.
• Prototype flows and prompts to validate direction before full builds.
• Collaborate with Technical Account Managers to keep implementations moving.
• Refine solutions with engineering during in-market use and surface edge cases.
• Identify recurring patterns and contribute to the product roadmap.
• Document decisions and process changes to preserve institutional knowledge.
• Support discovery, design, and handoff across multiple deployments.
• Communicate risks, blockers, and trade-offs as they arise.
• Stay current with AI tooling and bring new prototyping techniques to the team.

Skills

Product management

Business analysis

Solutions consulting

Forward-deployed

Implementation

Job description

Code and Theory seeks an Forward Deployed Product Manager to be embedded in implementation pods, translating client and internal needs into clear product requirements during platform rollouts. You will bridge product, delivery, and implementation teams, defining workflows, prototyping solutions, and shaping how The Machine's agents behave in real-world use.

You will own end-to-end workstreams, moving between client context, product thinking, and delivery realities, and you will create structure