Applied AI Engineer (Autonomous Defense)

Join our team as an Applied AI Engineer (Autonomous Defense) and work on building agents that turn offensive knowledge into defensive action. You will be responsible for developing reasoning systems that map proven attack paths to specific control changes, designing counterfactual experiments, and creating a safety architecture for autonomous change. This role offers growth opportunities, an innovation-driven culture, a flexible work environment, and an inclusive and diverse team.

• Construire des agents qui raisonnent à partir d'un chemin d'attaque prouvé pour apporter des modifications de contrôle spécifiques.
• Transformer les données de test d'intrusion en données d'entraînement et d'évaluation, en extrayant le signal de pourquoi une attaque a réussi dans un environnement.
• Concevoir et exécuter des expériences contrefactuelles dans des environnements de test représentatifs pour évaluer l'efficacité des changements.

• Hands-on experience with at least one of: post-training large language models (supervised fine-tuning, distillation, preference optimization, RL), or designing agentic systems with tool use, planning, and long-horizon execution that hold up outside a demo
• A track record of building evaluation systems for open-ended tasks where there is no clean label and success is judged by outcome
• Strong ML engineering experience building, evaluating, and deploying production AI systems, with hands-on work in deep learning, transformer models, and PyTorch
• Ability to independently research unfamiliar systems and rapidly become the team's expert
• Strong written and verbal communication, including clear technical documentation
• Experience with data pipelines, distributed systems, and cloud infrastructure, preferably AWS
• Experience reasoning over structured, heterogeneous, messy real-world data — configurations, graphs, logs, policy documents — rather than clean benchmark datasets
• Strong software engineering fundamentals and a track record of shipping and maintaining production-quality code in Python, not just scripts and proofs of concept
• Ability to work across model behavior, APIs, and infrastructure, and to collaborate closely with attack engineers, detection engineers, product, and infrastructure
• Master's in Computer Science, Machine Learning, or a related field, or equivalent practical experience, plus 4+ years of professional engineering experience
• You do not need to have been a pentester or a SOC analyst. You do need to be seriously interested in how attackers and defenders actually operate, and willing to learn it in depth. The reasoning we are building cannot be designed by someone who does not understand the domain
• Background in detection engineering, purple teaming, security engineering, offensive security, or incident response
• Hands-on familiarity with security control planes and their APIs and policy models: EDR (CrowdStrike, SentinelOne, Defender), firewalls and segmentation (Palo Alto, Fortinet), identity and conditional access (Entra ID, Okta), SIEM and detection content (Splunk, Sentinel), cloud IAM, WAF, MDM, and GPO
• Experience with causal or counterfactual inference, or with graph reasoning, planning, and search over large state spaces. Familiarity with Neo4j and attack-path analysis
• Experience building automation that takes write actions in production systems, along with the safety and change-management machinery around it
• Experience with adversarial robustness or prompt injection, particularly where an agent consumes untrusted input from the environment it operates in
• Experience integrating ML into production, multi-tenant SaaS, or running ML systems in customer-controlled or air-gapped environments