Applied Science - Cybersecurity

Mistral AI is seeking Applied Scientists to build frontier cybersecurity models. You will develop SOTA models across different domains of cybersecurity, from vulnerability analysis to incident response and remediation. You’ll build the environments, training-data pipelines, and evaluations that make these capabilities real and measurable. You'll work closely with cybersecurity actors and real production data, as well as cross-functionally with science, engineering, and product teams teams to deliver high-impact, trustworthy AI for security.

We are made of researchers, engineers, product builders, ex-founders who love tackling the most complex real world problems in the industry. You might come from security with a genuine interest in AI science, or from AI science with a genuine interest in security, both paths fit. What matters is curiosity for the side you know less well, and the drive to go deep on it.

What you will do

• 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.

• Fluent in English with excellent communication skills: at ease explaining complex technical to both technical and non-technical audiences.

• 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.

• Not afraid of contributing to a big codebase; finds their way around independently with little guidance.

• Writes clean, readable, high-performance, fault-tolerant Python code.

• Doesn’t need roadmaps: moves independently and navigates uncertainty with confidence.

• 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.