Vice President, Engineering

Submittable is hiring a Vice President of Engineering who is deeply technical, works hands-on, and builds with AI every day. You will report to the CTO and run engineering day to day: delivery, quality, reliability, cost, and the people who make them happen.

We want a different VP than the old SaaS playbook produced. That playbook grew output by growing headcount and adding layers of management. We believe AI coding tools now let a small team of strong engineers ship what once took a team several times its size. We intend to run engineering that way. You will keep the organization lean, keep layers few, and raise output per engineer rather than the number of engineers.

You cannot lead that change from a slide deck. You need to know firsthand what it is like to ship production software with AI agents, including where they fail. We expect you to have done this yourself in the last 12 months and to keep doing it here. Plan on roughly a fifth of your week going to hands-on technical work: reading and writing code, reviewing pull requests, working incidents, and building the tools your teams use.

We are choosing depth over span. This is a hands-on leadership role for a small, senior team, not a role leading a large organization through layers of managers. If you want to make architecture calls, contribute and ship code, and help a lean team deliver well beyond its size, we want to talk.

• Run engineering day to day. Turn the CTO's technical strategy and the product roadmap into plans, priorities, and shipped software.
• Stay hands-on. Review pull requests, prototype, debug production issues, and write tools. You should be able to pick up a ticket and ship it. Teams should trust your calls because of your technical judgment, not your title.
• Make AI the default way we build. Expect every engineer to work with AI coding agents (tools such as Claude Code, Cursor, and GitHub Copilot) for design, coding, testing, and review. Show people how by doing it yourself, and remove whatever stops them.
• Measure what AI changes. Track the four DORA metrics (how often we deploy, how long a change takes to reach production, how often changes fail, and how fast we recover), plus defects that reach customers and engineering cost per release. Report plainly on where AI helps and where it creates new problems, such as oversized pull requests, thinner review, or code no one on the team understands.
• Keep the organization lean. Grow output faster than headcount. Keep management layers thin, expect managers to stay technical, and favor small teams with clear ownership over large teams that need heavy coordination. Before adding a person, ask whether better tooling or automation solves the problem.
• Automate the toil. Use AI and automation for test generation, CI/CD checks, code review assistance, release notes, incident triage, and alerting, so engineers spend their time on the product.
• Raise the technical bar. Set standards for architecture, code review, testing, CI/CD, release management, and incident response. Shipping more code, faster, raises the stakes for review, testing, and observability. Make sure speed does not cost us quality.
• Own platform reliability, security, and cost. Our products are multi-tenant SaaS running on AWS and Azure, with Kubernetes. Work with infrastructure and security partners to meet uptime targets, pass SOC 2 audits, and keep cloud spend growing slower than revenue. Simplify our architecture where consolidation pays for itself.
• Build AI into the product. Work with the CTO and Product to add AI to Submittable so customers can set up programs, review applications, and make funding decisions in less time. Help decide what to build and how to test it for accuracy and fairness, because real applicants' outcomes depend on it.
• Hire and grow a small, strong team. Recruit engineers and leaders who already build with AI. Coach managers to stay close to the work. Build career paths that reward impact and technical depth, not team size.
• Partner across the company. Work with Product and Design on scope and trade-offs among new features, technical debt, and platform health. Work with Customer Experience and Support to resolve escalations and reduce customer-facing defects.

• You have shipped software with AI in the last 12 months. You personally built and shipped production code using AI coding tools within the past year. You can tell us what you built, which tools you used, what broke, and how it changed the way you work. This is a requirement, not a preference.
• Deep technical skill. 15+ years in software engineering, with real depth in distributed systems, API design, data modeling, cloud infrastructure (AWS and Azure, Kubernetes), and DevOps. You can hold your own in an architecture review with our strongest engineers and spot the flaw in a design or a pull request.
• You have run lean, high-output teams. 7+ years in engineering leadership, including at least 3 years leading other managers. You have raised a team's output without matching growth in headcount, and you can show the numbers: delivery metrics, team size, and cost, before and after.
• B2B SaaS at scale. Experience building and running multi-tenant, cloud-based B2B SaaS products. Experience consolidating platforms or integrating acquired products is a plus.
• Predictable delivery. A record of shipping complex software on predictable timelines without trading away quality, security, or reliability.
• Distributed teams. You have led teams across multiple countries and time zones, including offshore or nearshore partners. You write clearly, because much of this work happens in writing.
• Comfort with fast change. The norms for building software now shift every few months. You form a view, test it, and change your mind when the evidence says so.
• A clear storyteller. You can explain technical trade-offs to executives and non-technical colleagues in plain language. You tell people where engineering is going and why, often enough that they repeat it.
• A builder of strong teams. A track record of hiring, developing, and keeping strong engineers and building inclusive teams.
• Nice to Have: Experience in a private equity-backed or high-growth SaaS company; familiarity with SOC 2 and GDPR; experience shipping LLM-based features to customers, including how to evaluate them.
• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Location: This is a remote, U.S.-based role — you must live and reside in the U.S. full-time. 

Salary Details: The annual salary range for this position is $220,000 - $260,000. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. We also have a location-based compensation structure; there may be a different range for candidates in other locations.