Description review
Research Engineer - Cybersecurity (RL Environments)
Mistral AI · Remote - Paris · back to the listing
HR standards
66/100
needs work
Title ↔ description
75/100
solid
Reads as
Machine Learning Engineer
81% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
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What the listing never says
- 17 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
About Mistral
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.
The Role
Cybersecurity is one of the areas where AI could do the most good — and one where the capability must be built with particular care. As a Research Engineer on this track, you'll push our models' capabilities in vulnerability discovery and remediation, security analysis, incident response, and across the offensive-to-defensive spectrum of cybersecurity, and it will be your job to develop that ability deliberately and safely.
The work sits at the meeting point of research and engineering: you'll devise new approaches and be the one to implement them. Concretely, that means designing and building RL environments that reproduce realistic security scenarios, pipelines that generate synthetic training instances at scale, and experiments and evaluations that show what our models can actually do. Your results feed directly into the production training runs that shape our frontier models, in close collaboration with Mistral's researchers, engineers, and security specialists.
Security specialists and ML engineers are both welcome here — what matters is that you can hold your own in both worlds, and are eager to go deeper on the one you know less.
What You Will Do
• Design and implement RL environments simulating cybersecurity scenarios.
• Build the pipelines and infrastructure that generate synthetic cybersecurity training instances at scale — thousands of scenarios, not one hand-crafted exercise.
• Conduct experiments and evaluations of model capabilities on these environments, from quick prototypes to controlled benchmark runs.
• Own the infrastructure behind the environments: containers, sandboxes, VMs, cloud deployments, and orchestration (Kubernetes), all managed as code.
• Partner with researchers and security specialists across Mistral, distilling their domain expertise into reproducible environments and datasets.
• Write clear, efficient code in Python and enforce strong software-design practices: testing, code review, CI/CD.
What We're Looking For
• Hands-on expertise in offensive and/or defensive cybersecurity: vulnerability analysis, web/network/cloud security, secure coding practices, and SOC.
• Strong software engineering skills: clean, reliable, well-tested code — not just exploit scripts.
• Fluency in Python plus comfort reading lower-level languages (C/C++) for vulnerability analysis.
• DevOps experience: Docker, Kubernetes, cloud deployments, sandboxed or simulated environments.
• A pragmatic research-to-engineering mindset: ship a working first version, then refine and scale it.
• Working knowledge of RL techniques and LLM training methodologies, or strong motivation to develop it.
• Self-starter, low-ego, collaborative — comfortable working across research and engineering.
Nice-to-haves
• CTF, cyber-range, or bug-bounty experience — as a player, challenge author, or platform builder.
• A research background in cybersecurity, academic or industrial, or in another experimental discipline.
• Prior experience building RL environments or large-scale ML training infrastructure.
• Relevant open-source contributions and projects.
What We Offer
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.
Privacy Policy
Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy.
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.
The Role
Cybersecurity is one of the areas where AI could do the most good — and one where the capability must be built with particular care. As a Research Engineer on this track, you'll push our models' capabilities in vulnerability discovery and remediation, security analysis, incident response, and across the offensive-to-defensive spectrum of cybersecurity, and it will be your job to develop that ability deliberately and safely.
The work sits at the meeting point of research and engineering: you'll devise new approaches and be the one to implement them. Concretely, that means designing and building RL environments that reproduce realistic security scenarios, pipelines that generate synthetic training instances at scale, and experiments and evaluations that show what our models can actually do. Your results feed directly into the production training runs that shape our frontier models, in close collaboration with Mistral's researchers, engineers, and security specialists.
Security specialists and ML engineers are both welcome here — what matters is that you can hold your own in both worlds, and are eager to go deeper on the one you know less.
What You Will Do
• Design and implement RL environments simulating cybersecurity scenarios.
• Build the pipelines and infrastructure that generate synthetic cybersecurity training instances at scale — thousands of scenarios, not one hand-crafted exercise.
• Conduct experiments and evaluations of model capabilities on these environments, from quick prototypes to controlled benchmark runs.
• Own the infrastructure behind the environments: containers, sandboxes, VMs, cloud deployments, and orchestration (Kubernetes), all managed as code.
• Partner with researchers and security specialists across Mistral, distilling their domain expertise into reproducible environments and datasets.
• Write clear, efficient code in Python and enforce strong software-design practices: testing, code review, CI/CD.
What We're Looking For
• Hands-on expertise in offensive and/or defensive cybersecurity: vulnerability analysis, web/network/cloud security, secure coding practices, and SOC.
• Strong software engineering skills: clean, reliable, well-tested code — not just exploit scripts.
• Fluency in Python plus comfort reading lower-level languages (C/C++) for vulnerability analysis.
• DevOps experience: Docker, Kubernetes, cloud deployments, sandboxed or simulated environments.
• A pragmatic research-to-engineering mindset: ship a working first version, then refine and scale it.
• Working knowledge of RL techniques and LLM training methodologies, or strong motivation to develop it.
• Self-starter, low-ego, collaborative — comfortable working across research and engineering.
Nice-to-haves
• CTF, cyber-range, or bug-bounty experience — as a player, challenge author, or platform builder.
• A research background in cybersecurity, academic or industrial, or in another experimental discipline.
• Prior experience building RL environments or large-scale ML training infrastructure.
• Relevant open-source contributions and projects.
What We Offer
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.
Privacy Policy
Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy.