Senior Applied ML Research Engineer, Agentic Security
Designs threat models and experiments for agentic AI security risks, builds prototypes with fine-tuned models and analysis tools, and turns research into scalable product defenses. Requires MS/PhD in CS/ML, production coding skills, and security mindset.
About the job
Responsibilities
- Define and validate threat models for agentic systems, identifying which tool characteristics must co-exist to enable data exfiltration and malicious state change, and how to break those combinations
- Design and run experiments: create synthetic environments like file systems and tools, create task distributions that have attack paths and apply different attack strategies
- Break (manually and using optimization algorithms such as RL) agentic systems
- Design and improve static and dynamic analysis methods that automatically map tool capabilities to risk across diverse tool ecosystems, and make those methods scale
- Turn research insights into product-facing capabilities: risk classification, automated guardrail generation, and quantitative threat measurement
- Build measurement tools: eval harnesses, monitoring, dashboards, and feedback loops that quantify security outcomes
- Build capability and regression evals
- Optimize systems for real-world constraints (latency, cost, reliability) without losing scientific rigor
Requirements
- MS or PhD in CS/ML (or equivalent research experience) and enjoy working under uncertainty
- Fine-tuned and evaluated models in practice and can reason about data quality, overfitting, evals, and deployment constraints
- Write strong production code, comfortable owning the infrastructure that makes agentic evals run end-to-end; care about reproducibility and instrumentation
- Motivated by security problems and enjoy thinking like both builder and attacker
- Reason about how capabilities combine into risk: not just individual vulnerabilities, but system-level attack surfaces across tool ecosystems
- Communicate clearly, iterate fast, and can hold a technical narrative from "hypothesis" to "shipped"
Compensation & Benefits
- Competitive salary + equity
- Remote-friendly, with preference for candidates based in Amsterdam, Paris, Poland, New York, or San Francisco
- Fully funded team retreats every 8 weeks
- Health insurance allowance for you and your dependents
- Wellbeing, learning, and home office allowances
Skills
Machine Learning, Fine-Tuning, Reinforcement Learning, Static Analysis, Dynamic Analysis, Python, Evaluation Frameworks, Dashboards, Monitoring, Agentic Systems
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