Who are we?

Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems.

We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that.

We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft.

We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us!

Role Overview

We're seeking a senior product leader to own Cohere's Inference as a Service business end to end. The product is sovereign, self-serve inference: a mid-market or enterprise buyer picks a model, a region and a GPU size, and is running the same afternoon with no procurement cycle and no infrastructure of their own. You'll decide which open-weights and first-party models we serve, in which regions and on which hardware, set the rate card they sell at, and carry the revenue and margin that follow from those calls. You own the platform around that endpoint as well, from the console and dashboards a customer administers their deployment through to the catalog of first-party and third-party models behind it.

You'll set the strategy and run the execution, which means choosing where to spend across serving efficiency, the self-serve console and regional capacity when all three want the same budget, and shipping the deployment framework that removes infrastructure barriers while holding the line on data residency, compliance and operational confidence. You'll do that across engineering, serving, finance, legal and GTM without formal authority over most of it, and you'll be accountable for the outcome rather than for the roadmap.

The timing is most of the opportunity. Every serious company is deciding where its models will run and what it costs to keep them running, serving demand is climbing faster than anyone can rack hardware for it, and very few of those buyers are willing to hand their data to an operator who can read it. We are starting this business from a standing start, with the models, the silicon and the encryption already in place and almost no one else able to offer the combination. You would be taking a product to market in the window where that advantage is still ours, building one of Cohere's major revenue lines from its first customer onward, and shaping how a generation of companies buys AI it can actually trust.

Responsibilities

Own the P&L for Inference as a Service: carry the revenue number, set the rate card, and keep blended margin above the floor as the catalog and the fleet grow.

Own the inference platform itself: the console and dashboards where customers see usage, spend, API keys, access control and attestation, and the value-add tooling that makes a managed endpoint worth more than a raw one.

Own the model inventory across first-party and third-party models: what enters the catalog, how it is grouped by provenance, what it is priced at, and when something is quantised, replaced or retired.

Set the roadmap and the investment case across serving efficiency, the self-serve surface and regional capacity, and make the call when those three compete for the same budget.

Build the commercial motion with GTM from the ground up: self-serve acquisition for the mid-market, a sales-led motion for enterprise, and the partnerships that monetise capacity we would otherwise leave idle.

Stay close to the market: talk to customers and developers about integration friction and unmet needs, track competitor pricing, catalogs and launches, and follow where models and hardware are heading, then turn that into a clear view of where we should place our bets and what it would take to win.