White Circle

4 weeks ago

Research Scientist/Engineer (Agentic Systems)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 22 hours agoStatus: Live

Job Summary

Research scientist to build autonomous, large-scale environments that stress-test LLM agents (single and multi-agent) to failure, study breakdowns, and publish actionable insights. You will design adversarial, multi-agent environments, run end-to-end experiments, and turn findings into internal models and public writeups.

Location: Paris, London

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Build adversarial environments for agents to probe failure modes and misalignment.
•Construct realistic multi-agent environments and instrument them so emergent breakdowns are observable.
•Run end-to-end experiments against external APIs and internal models, orchestrating many agents in parallel.
•Catalogue concrete agent failure modes and develop tooling to surface them at scale.
•Translate experimental findings into internal models of agent behavior and public writeups.

Pay and Benefits

Salary: USD 150,000 - 250,000 annually

Equity and Bonus:Equity

Perks:Health InsuranceRelocation

Key Requirements

•Experience building non-trivial agent environments or automated research pipelines that run end to end (single- or multi-agent) and can discuss what broke and why.
•Strong software and AI engineering skills; ability to independently orchestrate many agents and containers in parallel without the orchestration becoming a 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.
•Ability to define questions and run falsifiable experiments when there is no playbook, handling fuzzy concerns effectively.
•A background with frontier models and coding agents in daily work (AI power-user).

Experience:AI safetyAgentsReinforcement learningLarge language modelsMulti-agent systems

Skills:PythonContainersAPIsLLMsFrontier modelsOrchestration

Languages:English

Tech Stack:PythonDockerKubernetesLLMsAPIs

Company Brief

White Circle

Builds AI-driven products and services to help businesses automate workflows, extract insights from data, and improve decision-making using machine learning and natural language processing technologies.

Industry: AI & Machine Learning

Website

Loading job details...

Preparing the role view and application actions.

White Circle

4 weeks ago

Research Scientist/Engineer (Agentic Systems)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 22 hours agoStatus: Live

Job Summary

Location: Paris, London

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Build adversarial environments for agents to probe failure modes and misalignment.
•Construct realistic multi-agent environments and instrument them so emergent breakdowns are observable.
•Run end-to-end experiments against external APIs and internal models, orchestrating many agents in parallel.
•Catalogue concrete agent failure modes and develop tooling to surface them at scale.
•Translate experimental findings into internal models of agent behavior and public writeups.

Pay and Benefits

Salary: USD 150,000 - 250,000 annually

Equity and Bonus:Equity

Perks:Health InsuranceRelocation

Key Requirements

•Experience building non-trivial agent environments or automated research pipelines that run end to end (single- or multi-agent) and can discuss what broke and why.
•Strong software and AI engineering skills; ability to independently orchestrate many agents and containers in parallel without the orchestration becoming a 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.
•Ability to define questions and run falsifiable experiments when there is no playbook, handling fuzzy concerns effectively.
•A background with frontier models and coding agents in daily work (AI power-user).

Experience:AI safetyAgentsReinforcement learningLarge language modelsMulti-agent systems

Skills:PythonContainersAPIsLLMsFrontier modelsOrchestration

Languages:English

Tech Stack:PythonDockerKubernetesLLMsAPIs

Company Brief

White Circle

Industry: AI & Machine Learning

Website