Machine Learning Scientist I/II, Scientific Reasoning
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Lilasciences · Cambridge, UKPrepare for this interview
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Your Impact at LILA As a Machine Learning Scientist focused on Scientific Reasoning, you will help pioneer the next generation of AI systems capable of reasoning like a scientist. You'll design novel frameworks that push the boundaries of LLM-based reasoning methods - while also implementing scalable frameworks that integrate with Lila's platforms. This role bridges deep theoretical thinking with practical ML engineering, enabling breakthroughs in how scientific hypotheses are generated, tested, deployed and optimized. What You'll Be Building Design and formalize frameworks for scientific reasoning with LLMs , including structured prompting, reasoning chains, and test-time compute. Explore and implement methods for in-context learning, self-reflection, and adaptive reasoning in scientific discovery workflows. Build scalable model prototypes that can be deployed to solve frontier scientific problems. Collaborate with scientists and engineers to encode domain knowledge into reasoning systems that integrate symbolic and statistical approaches. What You'll Need to Succeed PhD (preferred) or equivalent research/industry experience in Computer Science, Machine Learning, AI, Engineering, Materials Science or related fields. Strong programming skills in Python with deep expertise in LLM frameworks (PyTorch, HuggingFace Transformers, LangChain, LlamaIndex , and related toolkits). Expertise in LLM reasoning methods : in-context learning, test-time compute, chain-of-thought, or tool-augmented reasoning. Ability to balance theoretical research with practical ML engineering to deliver scalable solutions. Bonus Points For Research experience in causal reasoning, symbolic AI, or probabilistic programming . Contributions to open-source LLM reasoning frameworks . Familiarity with scientific discovery pipelines in chemistry, biology, or materials science. Experience with multimodal reasoning (e.g., combining text, image, and experimental data). Publications in top ML/AI conferences (NeurIPS, ICML, ICLR, ACL).
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