Lead Product Manager, Inference
Mistral.ai ParisEst. Est. EUR 70,000–100,000 / yearSenior
Estimated range based on role, country and industry — not published by the company.
Key requirements
- Product Management
About Mistral
Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms.
We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited.
The role
Mistral is building a sovereign AI cloud. Our inference platform already serves production traffic at scale. We are hiring a Lead Product Manager to own the inference portfolio within our Cloud & Inference organization.
You'll set strategy, write specs, sit with customers running production traffic, and work across our inference, infrastructure, and go-to-market teams to deliver real customer impact. Example problems you may work on include figuring out the next inference product we should launch to help our customers scale their AI workloads or how we cut fraudulent free-tier usage without adding friction for legitimate users.
You will work on state of the art infrastructure, in close collaboration with our science and research engineering teams. Your job is to turn that infrastructure into products that AI-native startups and enterprises pay for, integrated with our developer platform Studio.
You will operate as an individual contributor first — taking end-to-end ownership of complex and significant product lines — while building the foundation for the inference PM team as the portfolio grows. The size and shape of the team reporting into the role will be defined together with the VP Product once you're in place; from day one the expectation is hands-on product leadership, not people management.
You will be the first PM fully dedicated to inference and the most senior product hire in the space. You will report to the VP Product - Cloud & Inference, and work day-to-day with our inference engineering teams in the US and Europe.
What you'll do
Strategy and vision: define the vision, strategy, and annual goals for the inference portfolio, aligned with customer needs and company objectives. Benchmark the managed-inference field and pick where we win.
Roadmap and execution: own the full lifecycle for serverless inference, dedicated endpoints, and adjacent services. Prioritize, ship, measure, iterate. Economics: own adoption, revenue contribution, and gross margin for your products. Treat cost-per-token, unit economics, and capacity tradeoffs as product decisions, and partner with GTM, Finance and Marketing on pricing, packaging, and launches.
Customers: spend real time with AI-native startups and enterprises running advanced production workloads. Turn their constraints, like latency, throughput, compliance, and cost, into roadmap choices.
Engineering partnership: work daily with inference engineering and the cloud infrastructure teams behind our data centers on capacity planning, scheduling, routing, and reliability.
Research and open source: track open-source inference advances and emerging model architectures, and turn them into performance and efficiency gains for the product.
Team building: as the portfolio scales, help shape and grow the inference PM team — hiring, mentoring, and setting the product bar.
What we're looking for
Deep product experience in tech and infrastructure with a track record of bringing inference, cloud infrastructure, or large-scale distributed systems to market. You have built robust, at-scale AI infrastructure first-hand.
An engineering background before moving into product management.
You have taken an infra or inference product from early stage to material revenue and adoption, owning complex
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