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

About Relation

Relation is an end-to-end biotech 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 directly from patient tissue, functional assays, and machine learning to drive disease understanding—from cause to cure. We embarked on an exciting dual collaboration with GSK to tackle fibrosis and osteoarthritis, while also advancing our own internal osteoporosis programme. Combining our cutting‑edge ML capabilities with GSK’s deep expertise underscores our commitment to pioneering science and delivering impactful therapies to patients.

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. You will focus 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.

Your Responsibilities

• 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.

Systems Experience

• 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.

Professional Qualifications

• 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.

Desirable Knowledge or Experiences

• 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.
• Publication or open‑source contributions related to LLMs, reinforcement learning, agentic systems, or applied AI.

Personal Traits

• A strong, creative problem solver who enjoys tackling complex and ambiguous challenges.
• Comfortable working across both research and applied ML engineering.
• Collaborative and excited to work in interdisciplinary teams with scientists and engineers.
• Curious, pragmatic, and motivated to push the boundaries of applied AI.
• Driven by the opportunity to have a real impact on patients.

Join us in this exciting role where your contributions will have a direct impact on advancing our understanding of genetics and disease risk, supporting our mission to get transformative medicines to patients. Together, we’re not just doing research; we’re setting new standards in the field of machine learning and genetics. The patient is waiting!

Relation is a committed equal opportunities employer.

Recruitment Agencies

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