Applied Science - Cybersecurity

Mistral AI is seeking Applied Scientists to build frontier cybersecurity models. The role involves developing SOTA models across different domains of cybersecurity, from vulnerability analysis to incident response and remediation, and building the environments, training-data pipelines, and evaluations that make these capabilities real and measurable. The position works closely with cybersecurity actors and real production data, and cross-functionally with science, engineering, and product teams to deliver high-impact, trustworthy AI for security. Candidates may come from security with an interest in AI science, or from AI science with an interest in security.

Responsibilities

Develop, pre-train and post-train domain-specialized foundation models for agentic defensive security; Work across a diverse set of complex real world problems to build the future of AI for cybersecurity; Design evaluations and benchmarks that measure realistic security capability on customer-representative tasks; Build sandboxed execution environments, RL tasks, and verifiers grounded in real customer workflows, and generate and curate the training data for training frontier models; Develop the necessary tools and frameworks to facilitate data generation, model training, evaluation and deployment; Own technical delivery across project sprints and manage research communications with customer teams

Requirements

Fluent in English with excellent communication skills; Experience building harnesses, benchmarks or RL environments for code, agentic or security tasks; Hands-on security background in one or more of: vulnerability research, exploit development, red teaming, or detection and incident response; Comfortable contributing to a big codebase and finding their way around independently with little guidance; Writes clean, readable, high-performance, fault-tolerant Python code; Moves independently and navigates uncertainty with confidence without needing roadmaps; Low-ego, collaborative and eager to learn; Track record of success through personal projects, professional projects or academia

Nice to have

PhD / Master in a relevant field (Mathematics, Security, Machine Learning); Exceptional candidates from other backgrounds welcome; Contributions to a large open-source or industry codebase; Publications in top ML/security venues and/or contributions to open-source security or observability tools; Experience working on large code bases or large machine learning models at scale; Enjoys improving existing code: typing, tests, CI pipelines

Benefits

Healthcare coverage; Parental leave; Retirement plans; Relocation support; Wellness programs; Meal and transportation allowances; Other location-specific perks