VP, Software Engineering

Introduction

The VP, Software Engineering is responsible for realizing product vision across one or more of Optimizely's core product lines or broad cross-cutting horizontals — with a sharp focus on how AI shapes how we build, operate, and continuously improve. They are trusted, respected engineering leaders who drive personnel management, program management, engineering operations, technical excellence, and strategy development across their focus area.

This role sits at the center of Optimizely's AI-forward engineering culture. The VP champions the adoption of AI-assisted development practices — from code generation and automated testing to intelligent code review and developer tooling — ensuring engineering teams work smarter and ship faster. They also own the internal operating model for Engineering: how we run, how we measure ourselves, and how we get better over time.

They balance technical depth with a bias toward execution, process, and delivery. They are strong collaborators who build cross-organizational consensus, negotiate priorities, share information openly, and drive broad initiatives to meet commitments. They act as leaders and active participants in company-wide efforts that deliver business value and drive continuous improvement, and serve as exemplars of Optimizely's values.

Job Responsibilities

AI-Assisted Development and Tooling

• Champions the adoption of AI coding tools (e.g. GitHub Copilot, Claude, Cursor) across engineering teams, establishing clear standards, enablement programs, and measurable impact metrics.

• Drives the evaluation, rollout, and governance of AI-assisted development practices — including code generation, automated code review, test generation, and documentation — ensuring responsible and effective use at scale.

• Partners with product and platform teams to identify where AI can meaningfully accelerate delivery quality, developer experience, or operational efficiency.

• Stays current on the evolving AI development tooling landscape and shapes Optimizely's internal point of view on what to adopt, build, or avoid.

Engineering Operations and Internal Improvement

• Owns the operating model for Engineering: how teams are structured, how delivery is measured, how health is monitored, and how we improve cycle-over-cycle.

• Defines and drives engineering-wide metrics (delivery velocity, quality, incident response, system reliability) and builds the feedback loops that turn data into action.

• Leads cross-team retrospectives, post-mortems, and process reviews that result in durable operational improvements — not just one-off fixes.

• Drives internal engineering improvement initiatives: developer experience programs, productivity tooling, onboarding quality, and knowledge-sharing systems.

• Partners with the CTO to continuously improve the engineering team's overall structure, organization, and delivery of results.

Leadership, Strategy, and Delivery

• Directs delivery, operational, and/or people management for one or more product verticals and/or broad platform horizontals.

• Fosters an environment that treats sound architecture, high-quality implementations, and secure systems as first-class product requirements.

• Understands the business and operational context of our products and develops strategy to improve how we build and deliver software.

• Collaborates in organizational and technical decision-making, with contributions that weigh growth, cost, margin, time-to-market, quality, and future readiness.

• Coaches, mentors, and supports experienced engineering leaders, helping them become self-sufficient and solve their own challenges.

• Leads engineering retention, recruiting, employer branding, and organizational strategy in a growth environment.

• Participates as an amplifier of holistic organizational initiatives and company-wide direction.

• Drives value initiatives that extend beyond Engineering to benefit the broader business.

Knowledge and Experience

• Demonstrated history of implementing and managing significant software development programs.

• Hands-on experience adopting and scaling AI coding and developer productivity tools within an engineering organization.

• Familiarity with the AI development tooling landscape — including LLM-powered assistants, automated testing/review tools, and prompt engineering practices for software workflows.

• 5+ years of prior career experience as a software engineer.

• 12+ years of experience in engineering management.

• Proven track record running or significantly improving engineering operations: metrics programs, operating cadences, process design, and internal tooling.

• Strong background in cross-functional collaboration, organizational design, and driving execution at scale.

Education

Bachelor’s Degree (Computer Science or engineering preferred) or equivalent work experience