Engineering Manager
Mistral AI
Engineering Manager
Mistral AI
Open-weight LLMs and enterprise AI deployment
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.
Mistral is building a sovereign AI cloud for Europe. Our customers in finance, healthcare, and the public sector run frontier models on European-jurisdiction infrastructure, and the Cloud Platform team builds the systems that make that possible — compute orchestration, Kubernetes, bare metal, storage, networking, and the datacenter fleet that runs it all.
We run it all on our own metal, our own network, our own datacenters — which means when something breaks, the fix is ours to write.
European Compute Units let enterprises commit to multi-year capacity on that infrastructure, and we are scaling toward a gigawatt of compute across Europe by 2030. You will design and ship the layer every sovereign AI deployment depends on and shape the team building it.
This is a people-leadership role from day one. You will manage the team — hiring, coaching, performance, growth paths, honest feedback — while staying close to the code: roughly half your time in architecture decisions and the hardest technical problems the team faces, the other half leading your squad. If you are looking for a senior IC role, this is not it.
You will lead the team building our serverless containers product: the way developers run AI apps, MCP servers, APIs, and managed workers on our cloud without thinking about servers. Push a container, get an endpoint, scale from zero to thousands of instances and back down — across CPU and GPU.
On a hyperscaler, the serverless layer is a product someone else built. Here you own it end to end: the scheduler that decides where a container lands, the routing path a request takes, the machinery that turns a scaled-to-zero service into a warm instance before a customer notices. All of it runs on metal, network, and datacenters we own.
That tension is the product: scale-to-zero economics with cold starts in milliseconds, autoscaling that absorbs a launch spike without a config change, GPUs scheduled onto containers the way other platforms schedule CPU. The customers betting on this layer are the developers building the next generation of AI software on European infrastructure.
Own the container platform end to end: image delivery, placement, request routing, scale-up, scale-to-zero — across CPU and GPU.
Make cold starts a solved problem: snapshotting, prewarming, image-layer caching, whatever it takes for scale-from-zero to feel instant.
Treat AI workloads as the normal case: GPU-backed containers, streaming responses, long-lived connections, MCP servers that cost almost nothing while idle.
Write Go alongside your team and own the architecture and the hardest implementation decisions.
Grow the engineers, not just the software — set the standard for ownership and coach people past their comfort zone.
Own people management for the team: hiring, performance, growth paths, and honest feedback.
Partner with Product Managers to turn platform decisions into a product developers want to deploy on.
3+ years of real people leadership. You have managed engineers — hired, coached, given honest feedback, and grown them. Hands-on contribution matters here, but this role is judged on the team you build and lead, not just the code you write.
You have shipped production systems in Go. Not dabbled. Shipped.
You have built container or workload platforms — orchestration, scheduling, isolation, request routing — not just deployed onto someone else's serverless platform.
Deep Linux: namespaces, cgroups, image layers, page cache — comfortable well below the application layer.
Multi-tenancy instincts: density, isolation, and fairness when hundreds of customers share one fleet.
Significant experience building complex backend or infrastructure systems.
You have partnered with product, design, and business teams to ship software that reached real users.
A low-ego, team-first mindset. We care less about your exact domain and more about whether you are the kind of technical force other engineers want to work alongside.
We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks.
For the most up-to-date details on benefits available in your location, please refer to our Benefits page.
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Open-weight LLMs and enterprise AI deployment
Mistral AI builds open-weight large language models and the tools to run them, along with inference infrastructure and enterprise products that let organizations deploy AI with control over data residency, customization, and the underlying stack. Its platform provides open-model architectures and a deployment stack for inference, governance, and integration that can run on‑premises or in the cloud. This openness and modular stack differentiate it from single-vendor hyperscalers by prioritizing data locality and flexibility. The company aims to enable reliable, customizable AI deployment at scale for businesses and public-sector customers across many countries.
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