Director of Engineering

Moxie•New York, NY

1d•$207,000 - $253,000•Remote

About The Position

Moxie is seeking a Director of Engineering to lead the day-to-day operations of its Engineering organization, which includes roughly 60 engineers. The role owns delivery, organizational health, manager development, staffing, and engineering operating strategy, and leads AI strategy and execution across both the product and the software development lifecycle. The Director will partner closely with the Head of Engineering and Product leadership through the Product and Engineering operating cadence. The Director will lead seven Engineering Managers, become the first escalation point for cross-team delivery, staffing, and execution issues, and take ownership of delivery end to end. The role will refine Engineering performance and health measures—including Linear, DORA, and reliability metrics—and establish operating improvements that drive measurable gains in delivery predictability, cycle time, or reliability. The position also includes assessing AI usage, bringing AI or agentic initiatives into production, and embedding AI-assisted development into the Engineering operating model. The Director will guide an opinionated technical roadmap and deliberately allocate capacity across product delivery, architecture, platform investment, reliability, developer productivity, and technical debt, aligned with Product and company priorities. This is a hands-on technical leadership role requiring close engagement with architecture and the AI roadmap.

Requirements

12+ years of software engineering experience.
5+ years of engineering leadership experience.
At least 3 years leading managers and technical leaders.
Experience leading a 30–60-person engineering organization through multiple Engineering Managers.
Experience setting management standards, developing and coaching Engineering Managers, and directly handling performance, staffing, and organizational design decisions.
Experience building KPI systems such as DORA, cycle time, and reliability metrics that teams use to change behavior.
Ability to partner with Product leaders to surface risks, dependencies, and tradeoffs early.
Experience shipping production AI or agentic systems in a product or software development lifecycle, with standards for evaluations, observability, security, and cost, and ability to explain the work's ROI impact.
Strong technical judgment and comfort with system design, software design patterns, and engineering processes and principles such as agile, scrum, SRE, and DevOps.
Comfort reviewing and designing architecture and making capacity tradeoffs between feature delivery and platform investment.
Experience shaping engineering organizations, including hiring strategy, people management, SDLC maturity, and organizational design.
Experience working closely with founders and senior technical leaders, and bringing out the best in Engineering Managers and staff engineers.
Willingness to remain close to technical work rather than managing at arm's length.

Nice To Haves

Experience at a Series B–D product company.
Health technology or vertical SaaS experience.

Responsibilities

Lead the day-to-day Engineering organization of roughly 60 engineers, including seven Engineering Managers.
Own Engineering delivery, organizational health, manager development, staffing, and operating strategy.
Run the Engineering side of the Product and Engineering operating cadence in partnership with Product leadership.
Serve as the first escalation point for cross-team delivery, staffing, and execution issues, and own delivery end to end.
Refine and use Engineering performance and health measures, including Linear, DORA, cycle time, and reliability metrics.
Implement operating improvements and use KPIs to drive action and improve delivery predictability, cycle time, or reliability.
Assess AI usage across the product and software development lifecycle, and drive AI or agentic initiatives into production.
Embed AI-assisted development in the Engineering operating model and establish standards for production AI architecture, security, cost, and reliability.
Set and guide a technical roadmap, allocating capacity across product delivery, architecture, platform investment, reliability, developer productivity, and technical debt.
Develop and coach Engineering Managers and technical leaders, and make performance, staffing, hiring strategy, and organizational design decisions.
Stay sufficiently close to technical work to guide architecture and the AI roadmap and earn the trust of senior engineers and Engineering Managers.