Mistral AI

Customer Success Manager

• Manage a portfolio of enterprise customers and maximize the value of Mistral’s AI solutions
• Build trusted relationships with key stakeholders and tailor solutions to business objectives
• Lead customer onboarding, adoption, and ongoing engagement
• Monitor customer health metrics, identify risks, and proactively address challenges to prevent churn
• Collaborate with Sales on upsell and cross-sell opportunities
• Partner with Product and Engineering to gather customer feedback and influence the product roadmap
• Develop and deliver customer training, enablement programs, and best practices
• Conduct regular business reviews (QBRs)
• Advise customers on leveraging Mistral’s AI solutions to achieve business outcomes
• Work with Support and Product teams to ensure seamless experiences from deployment to production
• Create and execute customer success plans with KPIs, timelines, and measurable outcomes
• Advocate for customer needs internally
• Share customer insights with Product, Sales, and Engineering
• Clear obstacles during deployment and adoption to accelerate time-to-value
• Support renewals, contract negotiations, expansion discussions, and coordination with internal teams

Customer Success, EMEA

• Manage a portfolio of enterprise customers and maximize the value of Mistral’s AI solutions
• Build trusted relationships with key stakeholders and tailor solutions to business needs
• Lead customer onboarding, adoption, and ongoing engagement
• Monitor customer health metrics, identify risks, and address challenges to prevent churn
• Collaborate with Sales on upsell and cross-sell opportunities
• Partner with Product and Engineering to gather feedback and influence the product roadmap
• Develop and deliver customer training, enablement programs, and best practices
• Conduct regular business reviews (QBRs)
• Advise customers on leveraging Mistral’s AI solutions to achieve business outcomes
• Work with Support and Product teams to ensure seamless experiences from deployment to production
• Create and execute customer success plans with KPIs, timelines, and measurable outcomes
• Advocate for customer needs internally
• Share customer insights with Product, Sales, and Engineering
• Clear deployment and adoption obstacles to accelerate time-to-value
• Support renewals, contract negotiations, expansion discussions, and internal coordination

Customer Success

Research Engineer

• Turn real customer requirements into reliable training and deployment workflows
• Work end-to-end across model adaptation and post-training, evaluation, data, and infrastructure
• Build and improve post-training and evaluation workflows for CPT, SFT, RL, and distillation
• Develop tools and pipelines for synthetic data generation, data curation, training, evaluation, and deployment
• Debug and harden large-scale ML systems, including distributed training, scheduling/execution, checkpointing, observability, and reproducibility
• Improve the Forge codebase through clear APIs, tests, documentation, and maintainable abstractions
• Advance the RL training stack, including high-throughput asynchronous rollout and scalable post-training systems
• Ensure Forge deployment is seamless and adaptable across diverse clients, hardware, software stacks, cloud, and on-premises environments
• Partner with researchers and infrastructure engineers to translate bottlenecks into concrete system improvements
• Collaborate with scientists, engineers, product, and customer-facing teams to ship maintainable and trusted Forge projects

Research Engineer, Forge

• Develop services, APIs, controllers, and tooling for training, evaluation, fine-tuning, and batch inference
• Build systems for queueing, admission control, quotas, priorities, preemption, and topology-aware placement of GPU workloads
• Improve provisioning, allocation, and utilization of heterogeneous GPU resources across clusters
• Place workloads across multiple clusters based on capacity, data locality, hardware requirements, and organizational priorities
• Create self-service workflows for launching, observing, debugging, and reproducing distributed workloads
• Improve GPU utilization, scheduling latency, workload startup time, throughput, and infrastructure efficiency
• Develop observability, failure recovery, capacity planning, and operational tooling for critical ML workloads
• Participate in on-call rotations and troubleshoot applications, schedulers, networking, storage, and GPU infrastructure

Research Engineer, ML Platform

Recruitment

• Partner with Corporate and GTM leaders to understand hiring needs, define role specifications, and identify talent across EMEA markets
• Own full-cycle recruitment across Corporate and GTM functions, including Sales, Finance, Legal, People/HR, Marketing, and Communications
• Advise hiring managers on market mapping, leveling, compensation positioning, and the competitive EMEA talent landscape
• Create compelling, accurate, and locally relevant candidate outreach
• Own recruiting from sourcing through close for complex, high-volume roles
• Source passive and active candidates across EMEA talent pools
• Conduct intake and calibration sessions with hiring managers
• Build efficient, high-conversion pipelines and close senior candidates across geographies and languages
• Navigate cross-border hiring, relocation, remote-friendly offers, and country-specific employment frameworks
• Guide candidates through the hiring process and communicate Mistral’s mission, culture, and trajectory
• Explain Mistral’s AI positioning, open-weight philosophy, enterprise credibility, GTM motion, and corporate operations
• Deliver a high-quality candidate experience
• Develop and refine recruiting processes, tools, and documentation
• Analyze pipeline data and propose targeted improvements
• Build and maintain market maps, competitive intelligence, and talent pools
• Standardize interview rubrics, scorecards, and debrief practices across EMEA
• Collaborate with leadership and the Talent team to elevate hiring standards
• Contribute to Mistral’s EMEA talent acquisition strategy
• Partner on employer-brand and outbound sourcing initiatives

Talent Acquisition Specialist – GTM, Corporate

Artificial Intelligence

• Lead executive-level workshops to identify business challenges and AI opportunities
• Co-create AI adoption roadmaps with customers
• Develop business cases, quantify ROI, and align solutions with customer objectives
• Architect end-to-end AI solutions integrating Mistral models and platform into customer workflows and infrastructure
• Design, prototype, and deploy AI solutions in production with the Applied AI team
• Own pilot projects and proofs-of-value
• Advise customers on AI strategy and maximize investment value
• Monitor KPIs tied to business outcomes and communicate progress to executive sponsors
• Identify account expansion opportunities and build long-term partnerships
• Bridge customers and internal teams, synthesizing feedback for product and research roadmaps
• Develop reusable assets, best practices, and playbooks
• Travel approximately 30–60% to support client relationships and on-site deployment

AI Deployment Strategist

Associate

• Drive day-to-day treasury operations and automation across global entities.
• Manage daily cash positioning, concentration, and funding transfers across entities and currencies.
• Execute weekly payment runs and serve as the primary contact for manual payments and cash reconciliation.
• Own bank account openings, closures, signatory updates, and KYC/AML compliance.
• Participate in Kyriba implementation and automation for cash management, payments, and bank connectivity, including UAT and workflow configuration.
• Reconcile bank activity with the general ledger and maintain treasury documentation for audits.
• Contribute to short- and long-term cash forecasting, variance analysis, and ad hoc treasury projects.
• Support internal controls, guarantee management, and derivative/security data capture.

Senior Treasury Associate

Research Scientist

• Develop state-of-the-art models across text, image, and speech modalities
• Apply models and novel research methods to diverse use cases and domains
• Run pre-training, post-training, and deployment of models on clusters with thousands of GPUs
• Generate and curate pre-training and post-training data
• Work on evaluations and ensure model performance exceeds expectations
• Develop tools and frameworks for data generation, model training, evaluation, and deployment
• Collaborate with science, engineering, and product teams on complex use cases using agents and RAG pipelines
• Manage research projects and communications with client research teams
• Deliver high-impact AI solutions in collaboration with clients