Product Manager, Inference Platform

Who are we?

[Employer] 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. [Employer] 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 [Employer]'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 [Employer]'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.
• Define the metrics that tell us whether this is a business, including revenue, margin, win rate, time to first token and self-serve share, and lead the work that moves them.
• Partner with engineering, research, legal, finance, technical writing and developer relations to ship the platform and clear the licensing, pricing and capacity decisions behind it.
• Represent the business to the executive team with a recommendation, the evidence behind it and the trade-off it implies.

Requirements

• 10+ years of product management experience, including meaningful time owning a B2B infrastructure or platform product at the scope of a business rather than a feature area.
• Experience operating in a GM-like capacity: you have carried a revenue line or a P&L, set pricing, and been held to margin as well as to shipping.
• Background at a neocloud such as Together AI, Baseten or Fireworks, or at a hyperscaler such as AWS, Azure, OCI or Google Cloud, with a working view of how inference capacity is priced, packaged and sold, and a point of view on where the segment goes next.
• A track record of thinking about infrastructure as a product, with the ability to translate system-level capabilities into clearly differentiated, customer-facing features.
• Entrepreneurial, with experience taking a new product to market and building the commercial motion around it rather than inheriting one.
• Experience with deployment models such as multi-tenant and dedicated infrastructure, and ownership across the full product lifecycle, from problem definition through launch, iteration and eventual migration or deprecation.
• Analytical by default: you define success metrics before you build, instrument thoughtfully, and use data alongside qualitative feedback to make decisions.
• Able to set direction and earn alignment across engineering, infrastructure, finance, legal and GTM without formal authority, and to put a clear recommendation and its trade-offs in front of an executive audience.

Nice-to-Haves

• Experience owning a model serving or inference platform, including self-serve infrastructure and metered inference offerings.
• Exposure to data residency, sovereignty, or regional compliance requirements as they apply to AI or cloud infrastructure products, particularly in the inference-as-a-service context.
• Familiarity with the AI developer ecosystem and open standards, including orchestration frameworks such as LangChain and LlamaIndex, model serving frameworks such as vLLM, and an awareness of de facto interoperability standards and how they shape developer adoption and integration patterns for inference workloads.
• Experience with GPU infrastructure and cloud deployment models across both neocloud and hyperscaler environments.

Full-Time Employees at [Employer] enjoy these Perks:

• A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch.
• Full health and dental benefits, including a separate budget for mental health.
• RRSP matching, 401K, Pension Scheme.
• 100% Parental Leave top-up for up to 6 months, for either parent.
• Annual enrichment benefits:Arts & culture, fitness/wellness, quality time, and a workspace improvement credit.Education & learning stipend for conferences, courses, and coaching.

• 6 weeks of paid vacation (30 working days!)
• Budget for traveling to other offices if you are remote, plus an annual company offsite.

How and Where We Work:

• [Employer] is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon.
• For those in the office: a daily lunch program, plenty of snacks, and regular community and social events.
• For those not near an office: a co-working benefit so you can work alongside others in your city.
• Everyone receives a $500 home office stipend to set up your workspace properly.

If any of the above doesn’t line up exactly with your experience, we still encourage you to apply.

We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form, and we will work together to meet your needs.

We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider.

Beware of Scams: [Employer] will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the [Employer] careers page and LinkedIn only, with all communications from [Employer] employees coming from an @[Employer].com or @cw.[Employer] email alias. If jobs are viewed on other sites then please verify these through our official careers page.