Machine Learning Scientist - Reasoning Systems & RL (LLMs / Agents)

About Relation
Relation is a sector defining TechBio company developing transformational medicines, with technology at our core. Our ambition is to understand human biology in unprecedented ways, discovering therapies to treat some of life’s most devastating diseases. We leverage single-cell multi-omics from patient tissue, functional assays, and machine learning to drive disease understanding, from cause to cure.

We are scaling rapidly and building a team of exceptional individuals to push the boundaries of drug discovery. You will work in highly interdisciplinary teams where biology, computation, and engineering come together to solve complex problems that have not been solved before. Our state-of-the-art wet and dry labs in the heart of London are designed to accelerate this integration and translate insight into impact.

We are committed to building diverse and inclusive teams. Relation is an equal opportunities employer and does not discriminate on the basis of gender, sexual orientation, marital or civil partnership status, gender reassignment, race, colour, nationality, ethnic or national origin, religion or belief, disability, or age.

By joining Relation, you will help define how medicines are discovered and deliver meaningful impact for patients.

The Opportunity
Join the Turing team as a Machine Learning Scientist, where you will develop advanced AI systems that help scientists reason about complex biological problems.

This role focuses on building LLM-driven reasoning systems and intelligent agents, using approaches such as reinforcement learning, RLHF, symbolic reasoning, and agentic architectures. Rather than applying standard ML pipelines, you will work on training and shaping models that can reason over evidence, explore knowledge, and support scientific discovery.

You will work closely with computational scientists and biologists to develop systems that integrate large-scale biomedical data, scientific literature, and experimental insights to support target discovery and disease understanding.

This is an individual contributor role suited to a mid- to senior-level ML scientist who enjoys solving challenging applied research problems at the intersection of AI and biology.

Day to Day, You Will

• Design and develop agentic ML systems that can reason, plan, and interact with tools and data sources.
• Train and refine LLM-based reasoning models using approaches such as reinforcement learning, RLHF, or other alignment techniques.
• Develop algorithms that enable agents to explore and reason over complex scientific evidence.
• Build systems that integrate large-scale biological data, knowledge sources, and scientific literature.
• Collaborate closely with computational scientists, engineers, and biologists to translate scientific questions into ML systems.
• Prototype and iterate on new approaches for reasoning, decision-making, and hypothesis generation in scientific domains.
• Contribute to the technical direction of the team through experiments, publications, or new methodological ideas.

We are particularly interested in candidates who have previously built systems such as:

• Training reasoning or tool-using language models using RL, RLHF, or similar approaches
• Developing agents that plan, explore, and interact with tools or environments
• Designing learning loops where models improve through feedback or interaction
• Building multi-step decision-making systems (e.g., scientific discovery systems, robotics policies, simulation agents, or planning systems)
• Developing evaluation frameworks for reasoning or agentic models
• Applying advanced ML techniques to complex real-world domains such as science, robotics, healthcare, or autonomous systems

Professionally, You Will Have

• A PhD or MSc with substantial experience in Machine Learning, Computer Science, or a related quantitative field.
• Strong experience working with large language models, including training, fine-tuning, or evaluation.
• Experience with reinforcement learning, such as policy optimisation, actor–critic methods, or RLHF-style training pipelines.
• Hands-on experience building agentic or decision-making systems (e.g., tool-using LLMs, planning agents, or multi-agent systems).
• Strong programming skills in Python and modern ML frameworks.
• Experience developing applied ML systems in complex domains.

Bonus Experience

• Experience designing evaluation frameworks for reasoning or agentic systems.
• Experience applying ML to scientific, biomedical, or healthcare problems.
• Experience working in interdisciplinary environments combining ML and science.
• Publications or open-source contributions related to LLMs, reinforcement learning, agentic systems, or applied AI.

Personally, You

• Are comfortable working in a matrixed environment, balancing multiple stakeholders and contributing effectively across teams.
• Take ownership of your work, proactively seek opportunities to contribute, and enable others to do their best work.
• Communicate openly and directly, give and receive feedback constructively, and handle challenging conversations with respect.
• Actively seek out diverse perspectives, build strong working relationships, and contribute to shared goals across teams.
• Embrace challenges with openness and resilience, set high standards for yourself, and strive to deliver meaningful outcomes.

Working Style & Culture at Relation
At Relation, we operate in a matrixed, interdisciplinary environment, where impact is driven through collaboration across scientific, technical, and operational domains. We collaborate, and you will partner with colleagues across multiple teams and projects, contributing your expertise while aligning to shared company priorities. We work together and win together! The patient is waiting!

Recruitment Agencies
Please note that Relation does not accept unsolicited resumes from agencies. Resumes should not be forwarded to our job aliases or employees. Relation will not be liable for any fees associated with unsolicited CVs.

Relation is a committed equal opportunities employer.