You will design pre-training and RL data for engineering tasks while building agent architectures that enable models to execute complex, multi-step engineering workflows. You will also collaborate with domain experts to define evaluation criteria and diagnostic tools to ensure model competence in CAE and EDA fields.
You will lead three engineering teams focused on Managed Kubernetes, Network, and Storage while building the storage team from the ground up. You are responsible for setting technical roadmaps, managing team leads, and ensuring the reliability of infrastructure for high-stakes enterprise workloads.
You will design and build the end-to-end Kubernetes control plane on in-house infrastructure to support sovereign AI deployments. This includes managing system reliability, optimizing GPU scheduling, and automating the provisioning process from bare metal to production clusters.
You will build and automate the software delivery pipeline for large-scale datacenter infrastructure, including provisioning, configuration, and validation. Your goal is to minimize the time required to turn raw hardware into operational clusters serving customer AI workloads.
You will build and maintain secure, isolated, network-controlled sandbox environments for AI agents and reinforcement-learning workloads. This involves owning the architecture, security boundaries, and scaling infrastructure to support thousands of concurrent environments.
You will build and maintain a serverless container platform from the ground up, handling everything from image delivery to request routing and GPU scheduling. You will collaborate with product teams to ensure the platform meets the needs of developers building AI applications on sovereign infrastructure.
You will design and build the storage stack, including block, file, and object storage, to support sovereign AI cloud infrastructure. This involves writing services in Go, managing data plane operations, and planning capacity across large-scale datacenter environments.
You will develop AI-accelerated simulation capabilities by integrating solid-mechanics expertise into the training and deployment of AI Physics Models. This involves designing high-fidelity simulation campaigns, building automated pipelines, and collaborating with research and customer teams to ensure model accuracy against engineering standards.
You will lead a team of engineers in building secure, isolated sandbox environments for AI agents and reinforcement learning workloads. This role involves balancing people management duties with hands-on technical architecture and coding in Go.
You will lead a team to automate the delivery pipeline for datacenter hardware, turning racks into functional clusters. You are responsible for hiring and coaching the team while driving down the time-to-acceptance for new compute infrastructure.
You will own the full-cycle recruitment process for research and engineering roles, partnering with technical leaders to scale Mistral's US-based teams. Additionally, you will develop recruiting strategies, manage candidate pipelines, and establish best practices to attract top-tier AI talent.
You will own the full deal cycle for Mistral AI's public sector adoption across the Nordics, from strategic prospecting to closing and deployment. You will also cultivate relationships with C-level executives and guide customers through complex procurement and technical evaluation phases.
You will own the full-cycle recruitment process for Go-To-Market roles, partnering with leadership to scale revenue-generating teams. Additionally, you will develop recruiting operations, refine hiring best practices, and build talent pools to support the company's growth.
You will own and operate the infrastructure powering sovereign AI, including Linux environments and cloud systems. You will also collaborate with research and product teams to troubleshoot incidents and ensure high availability for AI workloads.
Design and build large-scale data center network systems focusing on leaf-spine architecture and multi-tenant AI infrastructure. Develop reliable control-plane software and automation workflows to manage network state and recover from failures.
Manage the full-cycle recruiting process for business and technical roles to support rapid company expansion. Partner with stakeholders to define hiring needs and implement effective sourcing and closing strategies.
Research and develop novel methods to advance LLM capabilities and scale cutting-edge AI models. Collaborate with cross-functional teams to design and deploy AI systems for reasoning, code, and multimodal applications.
Design, operate, and scale the large-scale Linux and HPC infrastructure powering Mistral's AI platforms. Automate operational tasks and optimize workload management to ensure high availability for research and production workloads.
