Role

We're looking for a hands-on engineering leader who can help define and operate the next generation of our AI-native engineering system, partnering directly with the CEO to evolve how software is built at Tread. You will partner directly with the CEO on building the category-defining operating system for construction materials logistics. The team you lead today is under 10 engineers shipping at aggressive velocity. We're investing heavily in an AI-driven SDLC - which you will help drive - with the product specs, architecture documentation, code review, and quality gates in place to make ~100% AI-generated code work. We've operated this way since March 2026.

The successful candidate will already have experience operating an AI-first engineering organization where coding agents are responsible for a significant portion of production software delivery. They should be able to describe:

• how work flows from specification through implementation using agents
• how architecture, code quality, and correctness are reviewed
• how evaluation frameworks prevent regressions and hallucinations
• how AI integrates into CI/CD and release management
• where human judgment remains essential

We are not looking for someone who is simply enthusiastic about AI. We're looking for someone who already operates an AI-native engineering organization and can install that operating system at Tread.

Tread is also a financial system of record. We move freight, we move money, and our customers reconcile their business against our platform. Correctness, reliability, and trust are non-negotiable.

In this role, you will:

• Own engineering execution across product engineering, platform, data, payments, AI infrastructure, internal developer tooling, and the AI-native software development lifecycle.
• Recruit top-tier talent and raise our bar for excellence. Push the team to strike the balance between speed and a reliable product. Have the hard conversations early.
• Drive the AI-native SDLC forward. Personally invest in the shared skills library, namespaced agent review pipelines, devcontainers, CI, and cloud agent infrastructure that lets a small team operate at scale.
• Design and continuously improve evaluation systems for AI-generated software, including quality gates, benchmark suites, regression testing, rollout strategy, rollback procedures, and model performance monitoring.
• Set technical direction across the Rails API, data layer, payments business logic, and clients.
• Make build-vs-buy and platform-investment calls that balance speed with long-term leverage.
• Own platform reliability and trust posture: uptime, data integrity, payment accuracy, security, and incident response.
• Partner closely with the CEO on the product roadmap and serve as a key thought partner. The roadmap includes deepening AI agents into customer workflows and embedding fintech throughout the app (instant funding, ACH for B2B payments, financing).

Requirements

• 7-12 years building software, with demonstrated engineering leadership in high-growth startups (Seed-Series B/C) or founder experience.
• Background in hypergrowth startups. Ideally Seed through Series C, or founder experience.
• Fluent and opinionated about AI-native engineering. You've personally built or operated production engineering workflows where AI agents participate throughout the software lifecycle, including specification, implementation, testing, code review, evaluation, deployment, and maintenance.
• Strong engineering judgment around speed vs. correctness. You know where to intentionally optimize for rapid iteration, where correctness is non-negotiable, and how to build systems that recover safely when things fail.
• Experience operating payments or financial systems at scale.
• Deep technical credibility. Comfortable discussing architecture, distributed systems, AI infrastructure, evaluation systems, developer tooling, and modern software delivery practices at every level of the organization.
• Lives in modern AI tooling every day. Claude Code, Cursor, coding agents, prompt engineering, context management, evaluation workflows, and multi-agent collaboration are part of your daily engineering practice-not technologies you've merely experimented with.
• Hiring instincts at the senior and staff level. You can identify, calibrate, and close engineers who are stronger than you in their domain.
• Engineers who've worked with you choose to work with you again, and we'll talk to them.
• Eager to work in-person at our San Francisco office 3-5 days/week.

Why You Shouldn't Work With Us

• You are not excited to work in-person.
• You cannot put in the hard work to build a $B business.
• You are not excited to design and operate an AI-native engineering system hands-on (agents, SDLC workflows, evals, CI/CD integration).
• You view AI as an assistant rather than a fundamental change in how engineering organizations should operate.
• You don't ship high-quality code, fast.

Technology

Current stack: Ruby on Rails, GraphQL, React, TypeScript, Flutter, PostgreSQL, AWS.

AI tooling is part of the infrastructure: a shared skills library across Claude Code and Cursor, namespaced agent skills for diff review and PR feedback, devcontainers, fast feedback loops, pre-approved permissions, and cloud agent support. We deploy multiple times per week, review PRs the same day, and invest in testing and correctness up front so on-call stays light. Improving the SDLC is first-class engineering work.

Interview Process

• Recruiter conversation
• Meeting with the CEO
• Meeting with the Engineering team
• Technical systems design and AI engineering case study focused on transforming an engineering organization using AI-native development practices.
• Board Member
• Reference conversations
• Offer