VP of Software Engineering

Key Responsibilities

• Engineering Leadership & Organizational Scale
• Lead and grow the software engineering organization, including hiring, org design, leadership development, and performance management.
• Build a high-accountability, high-trust engineering culture centered on ownership, speed, quality, and customer impact.
• Establish clear operating rhythms, decision-making frameworks, and delivery discipline so teams can execute effectively as the company scales.
• Develop engineering managers and senior technical leaders who can extend your impact across the organization.


• Technical Leadership & Product Delivery
• Own engineering execution across the product and platform, ensuring teams ship high-quality SaaS capabilities with speed, predictability, and strong operational performance.
• Guide architectural decisions and technical strategy across application, platform, data, and integration layers.
• Set and uphold strong engineering standards around code quality, testing, observability, reliability, security, and maintainability.
• Make pragmatic technology and investment choices that balance near-term delivery, long-term scalability, and business impact.


• AI-First Software Development
• Define and lead an AI-first engineering model in which AI is embedded into day-to-day software development workflows across the SDLC.
• Evaluate, implement, and standardize the use of AI tools across the SDLC (e.g., coding assistants, LLM-based code review, test generation, documentation, incident analysis, developer productivity tooling).
• Design workflows where AI is “in the loop” for:
• Requirements refinement and spec drafting
• Architecture exploration and design docs
• Code generation, refactoring, and modernization
• Test case creation and regression detection
• Documentation, runbooks, and onboarding material
• Production troubleshooting and operational analysis
• Establish guidelines, best practices, and guardrails for safe and effective AI use (e.g., security, privacy, IP, data handling, quality standards).
• Measure the impact of AI-enabled workflows and scale the practices that meaningfully improve throughput, quality, and team effectiveness.


• Org & Process Leadership
• Partner with product and company leadership to define how we plan, build, and ship software in an AI-first environment.
• Introduce and refine processes, operating cadences, and tooling that increase throughput, improve predictability, and reduce cycle time.
• Build an engineering organization that can scale without losing quality, urgency, or technical rigor.
• Create mechanisms for experimentation, continuous improvement, and shared learning across engineering.


• Coaching & Mentorship
• Coach engineering managers and senior technical leaders on organizational leadership, execution, and engineering quality.
• Help teams evolve their workflows from basic AI-assisted development to more sophisticated use of AI in complex engineering tasks.
• Provide guidance through architecture reviews, design reviews, operating reviews, and technical decision-making.
• Foster an environment of high standards, strong feedback, and continuous growth.


• Collaboration & Strategy
• Collaborate closely with product, design, and other stakeholders to ensure we’re solving the right problems and investing in the right technical capabilities.
• Help define the roadmap for platform, developer tooling, and engineering investments that unlock greater leverage over time.
• Communicate clearly with leadership about progress, risks, organizational health, and opportunities to increase engineering impact.
• Serve as a strategic partner in company planning, helping align engineering priorities with broader business goals.

Required Skills & Qualifications

• Must have 8+ years of professional software engineering experience, including significant leadership experience in SaaS or cloud software environments.
• Proven experience leading engineering teams and organizations in a scale-up environment, where both product complexity and organizational complexity are increasing.
• A track record of building and shipping sophisticated software products in a modern stack (e.g., cloud-native services, microservices/SaaS, APIs, data-heavy systems, workflow-driven platforms).
• Demonstrated experience leading organizational and technical evolution — e.g., scaling teams, improving engineering execution, platform migrations, process redesign, or adoption of new development practices and tools.
• Hands-on familiarity with AI/LLM tools for software development and strong judgment about where AI materially improves leverage, quality, and speed.
• Strong system design and architectural skills, with the ability to guide scalable, maintainable architectures and communicate trade-offs clearly.
• Excellent communication skills and the ability to lead through growth, complexity, and changing ways of working.
• Strong people leadership skills, including hiring, coaching managers, and developing senior engineering talent.

Preferred Qualifications

• Experience integrating AI- or LLM-based capabilities into production systems or internal engineering workflows.
• Background in developer productivity, platform engineering, or building internal tools and systems for engineers.
• Experience in a high-growth or scale-up environment where speed, ambiguity, and prioritization are central to success.
• Familiarity with data privacy, security, and compliance considerations in the context of SaaS platforms and AI-enabled tooling.
• Experience working on products that involve complex workflows, intelligent automation, data-rich systems, or sophisticated integrations.