Machine Learning Scientist I/II,… at Lila Sciences · Cambridge

Summary: Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. As a Machine Learning Scientist focused on Scientific Reasoning, you will help pioneer AI systems capable of reasoning like a scientist and design frameworks that integrate with Lila’s platforms. Responsibilities: - 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 Required Qualifications: - 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 Preferred Qualifications: - 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) Required Skills: Python, LLM frameworks, LLM reasoning methods Important Skills: Machine Learning Nice-to-Have Skills: Theoretical research, Practical ML engineering, Causal reasoning, Symbolic AI, Probabilistic programming, Multimodal reasoning, Scientific discovery pipelines Benefits: Bonus potential, Generous early equity