Research Scientist/Engineer – Agentic Systems

White Circle

Research Scientist/Engineer designing environments to study AI agents' failures at White Circle. Building complex, large-scale settings for autonomous agents in Paris or London.

Posted 6/29/2026full-timeParis • 🇫🇷 FranceMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and orchestrating complex multi-agent environments, with a strong focus on empirical research and AI engineering. Capable of defining and executing experiments to analyze agent behavior and failure modes, while effectively communicating findings through published research.

Highest-signal resume keywords

Multi-Agent Environment DevelopmentEmpirical Research in AgentsAI EngineeringExperiment Design and ExecutionMonitoring for Model Failures

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Applicant Tracking System Keywords

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Hard Skills

Agent Environment DevelopmentAutomated Research PipelineOrchestration of AgentsEmpirical Research MethodologyExperimentationAI Model CodingBenchmark ReproductionFailure Mode AnalysisData AnalysisStatistical Modeling

Soft Skills

Problem-SolvingCommunicationCritical ThinkingCollaborationAdaptability

Tools & Technologies

External APIsAI ModelsResearch ToolsMonitoring ToolsData Visualization Tools

Certifications & Qualifications

AI Safety FellowshipMSc in Machine LearningPhD in Computer SciencePhD in Cognitive SciencePhD in Computational NeurosciencePhD in Physics

Industry Keywords

Agentic EnvironmentsRed-TeamingPost-TrainingAnomalous BehaviorModel FailuresFrontier ModelsAutomated ResearchQuantitative ResearchFalsifiable ExperimentsResearch Publication

About the role

Key responsibilities & impact

Build adversarial environments for agents: complex, uncertain settings that sit on the boundary of agent capability and alignment, where failure is informative rather than trivial.
Build realistic multi-agent environments and instrument them so emergent breakdowns are observable — failures that arise from the agents themselves, not ones scripted from the outside.
Run experiments end to end, against external APIs and our own models, orchestrating many agents in parallel.
Catalogue concrete agent failure modes and build the tooling to surface them at scale.
Turn findings into internal models of agent behaviour and into public writeups.

Requirements

What you’ll need

Have built at least one non-trivial agent environment or automated research pipeline that ran end to end (single- or multi-agent), and can talk through what broke and why.
Strong software and AI engineering. Can independently orchestrate many agents and containers in parallel without that orchestration being the bottleneck.
A track record of empirical research in agents, red-teaming, or post-training where you defined the question, ran it, and drew a defensible conclusion.
A fast empirical iterator who is comfortable defining the question when there's no playbook: can take a fuzzy concern ("do these agents collude under pressure?") and turn it into a concrete, falsifiable experiment.
An AI power-user — fluent with frontier models and coding agents in your daily work.
Published research at A* venues on automated red-teaming, agentic environments, or post-training.
Experience building monitoring for model failures and anomalous behaviour.
Experience reproducing public benchmark results and finding where the original methodology is fragile or misleading.
An MSc or PhD in machine learning, computer science, cognitive science, computational neuroscience, physics, or a related quantitative field.
AI safety fellowship (MATS, ASTRA, Anthropic Fellows, etc.), or a comparable self-directed research record.

Benefits

Comp & perks

Paid time off in line with your local regulations, no matter where you work from
Comprehensive medical insurance for our France-based team
All the hardware, tools, and services you need
Covered subscriptions for AI agents and IDEs
Team off-sites twice a year: we’ve recently been to the Alps and to Saint-Tropez