Director, Implementation (US)
$150,000–$225,000 year
On-siteNew York City, New York, United States
Job Summary
Director of Implementation to own end-to-end implementation of the Machine across customer and agency environments, from technical scoping through deployment, integration, and adoption – you will write code and lots of it – daily. Lead a team of implementation engineers and solution architects, set delivery standards, and be accountable for the quality and pace of every engagement. Translate customer requirements into deployment plans, surface gaps, prioritize integrations, and feed real-world needs back into the roadmap. Design integrations with tools customers run (Figma, Slack, Teams, Adobe, dashboards). Drive adoption past go-live with enablement, instrumentation, and follow-through. Collaborate across engineering, account, and customer stakeholders to keep delivery tightly aligned with customer outcomes.
Required Qualifications
- A track record of leading technical implementations or professional-services delivery for a software or platform product, ideally in environments with demanding enterprise customers
- Strong technical fluency across cloud, integrations, and APIs — enough to architect a deployment, debug an integration, and earn the trust of the engineers you lead
- Fantastic verbal communications and presentation skills, since this is a customer-facing role and you must have experience owning customer relationships through delivery, including the hard conversations about scope, timelines, and tradeoffs
- People leadership experience, including in matrixed or cross-functional structures where influence matters as much as authority
- A 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, including 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
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