Software Engineer, Leverage

A little about us…Fleetio is a modern software platform that helps thousands of organizations worldwide manage their fleet operations. Transportation technology is a hot market, and we’re leading the charge with raving fans and new customers signing up every day. We raised $450M in our Series D funding round in March of 2025 and are on an exciting trajectory as a company.

Fleetio is also a proud founding member of the Rails Foundation!

Fleetio is seeking a Staff Software Engineer to be the founding engineer on our new Leverage team. The Leverage team's mandate is to make every person at Fleetio more productive by building the knowledge infrastructure that connects our internal data to the AI tools employees already use every day. At other companies, this work falls under “productivity engineering” or “internal tools.”

We call it Leverage because the scope is broader: serve the entire company, not just engineering.

This is not a blank slate. Fleetio engineers are already using AI tools daily, and an organic ecosystem of shared skills and integrations has emerged across the engineering org. You will build on that momentum, not start from zero. Your job is to add the enterprise capabilities this ecosystem needs to scale: identity and auth, structured access to internal data, governance, and quality standards.

You will design and build the context layer that connects Fleetio's distributed knowledge to the AI tools our team already uses. You will work directly with the Leverage team lead, and together you will define the technical direction for everything this team builds.

This is a remote opportunity open to candidates in the United States, Canada, or Mexico.

You are a staff-level, systems-minded engineer who gets energy from building tools that make other people more effective. You have deep experience integrating across SaaS APIs and are comfortable navigating the messiness of inconsistent auth models, pagination, rate limits, and data formats. You would rather build one system that enables 50 integrations than build 50 one-off integrations.

You are comfortable operating in ambiguous, cross-functional spaces. You talk to users before you write code. You care about adoption, not just shipping, and you understand that internal tools fail when they have bad UX or no adoption strategy. You bring strong opinions about developer productivity, loosely held, and you communicate clearly with both engineers and non-technical stakeholders.

You are product-minded and team-oriented, willing to hear others' perspectives and educate on best practices. Excellent communication skills, particularly written, are essential.

• Design and build the knowledge infrastructure that gives AI tools at Fleetio structured access to company context. The form this takes (an API layer, a CLI, an SDK, agent configuration, protocol servers, or something new) is a decision you will help make.
• Build integrations that connect Fleetio's internal knowledge sources (project management, documentation, code, data warehouses, communication tools) into a consistent, secure context layer with identity passthrough.
• Design the system so other teams can extend it. Whether that means spoke templates, plugin architectures, or shared configuration patterns, the goal is to scale beyond what a small team can maintain directly.
• Partner with teams across Fleetio to identify where missing context creates the most friction, and build the integrations that close those gaps. This is not engineering-only:
  Product Managers need AI tools that understand product strategy and customer data; designers need access to design systems and user research;
  Customer Success needs account history and help center content.
• Take ownership of CI/CD intelligence, making pipelines fast, reliable, and trustworthy as AI-accelerated development increases code velocity.
• Define and own the measurement framework for AI adoption and internal tool impact across the company. Track what works, surface what doesn't.
• Champion AI-first documentation practices: structure internal docs so AI tools can consume them effectively (coding examples, machine-parseable formats, clear…