Senior Research Scientist Agentic AI Systems

European Tech Recruit; Paris; Sur place; EUR 60 000 - 90 000; Plein temps

Résumé du poste

A leading global technology innovator is looking for a researcher to develop foundational architectures for autonomous AI agents, focusing on memory and learning algorithms. The ideal candidate will hold a PhD in Machine Learning or a related field, have a strong publication record, and possess skills in reinforcement learning and memory-augmented systems. This role includes formulating research directions and collaborating with peers in a dynamic environment. Interested candidates are encouraged to apply or send their CV to [e-mail].

Qualifications

• PhD in Machine Learning, AI, or a closely related field.
• Demonstrated ability to independently define and execute research agendas.
• Strong mathematical foundation and experimental rigor.

Responsabilités

• Formulate novel research directions in agentic learning and autonomy.
• Design and analyse new architectures for memory and decision‑making.
• Collaborate with a small group of research peers.

Connaissances

Reinforcement Learning theory or algorithms

Memory‑augmented neural networks

Large language model adaptation

Continual learning

Strong publication record

Solid implementation skills (PyTorch, JAX)

Formation

PhD in Machine Learning or related field

Description du poste

Our client, a leading global technology innovator, is building a long‑term research program focused on the fundamental scientific challenges underlying autonomous AI agents.

The objective is to develop principled architectures and learning algorithms that enable agents to:

• Reason and plan over long horizons.
• Use tools and external systems effectively.
• Build and update structured memory over time.
• Improve their own policies through self-reflection and continual learning.

This role is intended for researchers who want to work on open, foundational questions at the intersection of:

• Memory‑augmented neural systems.
• Planning and hierarchical decision‑making.
• Continual and self‑improving learning systems.

Responsibilities

• Formulate novel research directions in agentic learning and autonomy.
• Design and analyse new architectures for memory, planning, and decision‑making.
• Develop scalable RL or hybrid learning algorithms.
• Conduct rigorous empirical evaluation and theoretical analysis.
• Collaborate with a small group of research peers in an intellectually dense environment.
• Mentor junior researchers or interns (depending on seniority).

Ideal Profile

• PhD in Machine Learning, AI, Reinforcement Learning, or a closely related field.
• Strong publication record in leading ML conferences or journals.

Deep expertise in one or more of:

• Reinforcement Learning theory or algorithms.
• Memory‑augmented neural networks.
• Large language model adaptation and alignment.
• Continual learning.
• Demonstrated ability to independently define and execute research agendas.
• Strong mathematical foundation and experimental rigor.
• Solid implementation skills (PyTorch, JAX, or similar frameworks).

Success Criteria

• Publication of influential work in top‑tier venues.
• Development of novel agent architectures or learning frameworks.
• Clear empirical or theoretical advances in agent autonomy.
• Establishment of new research directions in long‑horizon AI systems.

If your background aligns with this opportunity and you are driven to solve foundational challenges in autonomous AI, we encourage you to apply or email your CV to [e-mail].