relationrx - Machine Learning Scientist – Reasoning Systems & RL (LLMs / Agents)
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Requirements
• 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. • 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 • 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.
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. • 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 • 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.
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