Director, Implementation

Responsibilities

• Own end‑to‑end implementation of The Machine across customer and agency environments, from technical scoping through deployment, integration, and adoption – including daily code writing.
• Stand up and lead a team of implementation engineers and solution architects, setting delivery standards and accountable for the quality and pace of every engagement.
• Translate customer requirements into deployment plans, working closely with the platform engineering team to surface gaps, prioritize integrations, and feed real‑world needs back into the roadmap.
• Design integrations against tools such as Figma, Slack, Teams, Adobe, and performance dashboards, ensuring The Machine works alongside existing stacks.
• Drive adoption past go‑live, defining success metrics for each engagement and steering teams toward it with the right enablement, instrumentation, and follow‑through.
• Partner across engineering, account, and customer stakeholders, coordinating reporting lines and expectations so delivery holds together as one effort.

• Track record of leading technical implementations or professional‑services delivery for a software or platform product, ideally with demanding enterprise customers.
• Strong technical fluency across cloud, integrations, and APIs – able to architect deployments, debug integrations, and earn engineers’ trust.
• Excellent verbal communications and presentation skills, with experience owning customer relationships through delivery, including scope, timeline, and trade‑off conversations.
• People‑leadership experience, including in matrixed or cross‑functional structures where influence is as important as authority.
• Bias toward adoption and outcomes, with conviction about what separates a deployed tool from one a team actually uses.
• Comfort leveraging AI‑enabled development tools and workflows to accelerate engineering, automation, debugging, and operational tasks.
• Experience orchestrating multi‑step AI or agent‑driven workflows, selecting appropriate models, tools, and execution patterns for different use cases.
• Strong judgment reviewing and hardening AI‑assisted output for security, scalability, maintainability, and architectural fit.
• Experience building or maintaining prompts, evaluation frameworks, documentation, or operational context systems that improve engineering velocity and reliability.
• Familiarity with automated evaluation and feedback loops for AI‑enabled systems and workflows.

The base compensation range for this role is $150,000 – $225,000 and spans multiple levels. Compensation is influenced by skill set, level of experience, budget, and location.