Engineering Manager & Product Lead, Float Intelligence

This posting reflects a current, actively recruited opening at Float. All applications are being reviewed on a rolling basis.

About Float

Float is on a mission to simplify finance for Canadian businesses, empowering them to eliminate complexity and unlock new opportunities. Through our innovative platform, Float enables businesses to streamline financial operations and optimize cash flow, so they can focus on what matters most: growth.

As one of Canada’s fastest growing companies and top-rated startups in 2025 , 2024 and 2023 , Float is customer-obsessed, passionate and entrepreneurial, with a team that includes leaders from Uber, Stripe, Shopify, Top Hat, Ada, Doordash, Snowflake, and Wealthsimple.

At Float, everyone is an owner, bringing their unique perspective to our team and product. Your voice is important, and we take having a culture based on feedback seriously. We openly share our thoughts and differing opinions so we can continue to improve. We do our best to keep our decision-making decentralized so that all team members feel ownership in our success.

Our Product

Float is Canada’s intelligent financial operating system, combining modern financial services and software to help businesses spend, save, and grow. Trusted by more than 10,000 Canadian companies, Float provides high-limit corporate cards, automated expense management, next-day bill payments, high-yield accounts, and industry-leading support, all built in Canada, for Canada. Float recently announced our $85 million Series C , and is backed by world-class investors, including Inovia Capital, Growth Equity at Goldman Sachs Alternatives, OMERS Ventures, and Silicon Valley Bank.

Our team is a collection of ambitious, collaborative and mission-driven people from all walks of life but with one goal: helping Canadian companies not just survive but thrive. And we’re looking for bold innovators to help shape the future of business finance in Canada.

AI Use in Our Hiring Process

We use technology, including artificial intelligence (AI), to support parts of our hiring process. This may include AI-assisted scheduling and candidate communications, and AI-generated interview notes, guides or summaries to help our team focus on the conversation. All hiring decisions are made by our hiring team.

About the Role

Float is hiring a Founding Lead to own and accelerate Float Intelligence end to end, across the technical foundations, the data, and the product experiences, all under a single mandate. Float Intelligence is how we bring agentic AI finance to every business in Canada: a product that does the finance work our customers do by hand today, coding transactions, chasing receipts, checking spend against policy, and gathering invoices out of inboxes and vendor portals. We shipped the first version this year and it worked. Now we are building the rest of it, and we are building it fast.

You would lead a small, fast-moving team of product engineers and data/ML engineers, reporting directly to the Head of Product. This is intentionally a hybrid seat across Engineering and Product leadership. You own the roadmap for what gets built, and you make the technical calls: model choice, evaluation, what has to be deterministic, and what a launch is gated on. The team ships into other product teams' surfaces and builds net-new surfaces that put AI directly in front of customers.

This is a true end-to-end owner role. The people who do best here are ex-founders and highly capable technical leaders with strong commercial product taste, opinionated about both what to build and how to build it, and drawn to the highest-leverage, most cutting-edge AI work across Float's entire product surface.

What You'll Be Responsible For

Leading, growing, and developing a small team of product engineers and data/ML engineers.

Owning the roadmap for Float's AI experiences alongside the Head of Product, including proposing new bets.

Taking AI products from zero to one, from exploratory proofs of concept through to the experiences that reach customers at scale.

Making the technical calls: model and LLM approach, evaluation design, and the infrastructure for scale, latency, and inference cost.

Building the shared platform layer, where required for: extraction, evaluation, observability, agent and prompt frameworks, and data pipelines, that other product teams build on rather than rebuild.

Equipping internal product teams with the AI models and engines they need to ship the experiences they want, and building new customer-facing surfaces that expose those experiences directly.

Partnering with the product managers whose areas these experiences ship into, so each one lands well and ownership is clear afterward.

Deciding what to stop, since exploratory work only pays off when the things that are not working get killed quickly.

About You