Lila Sciences is looking to pioneer the next generation of AI systems capable of reasoning like a scientist to solve humankind's greatest challenges in human health, climate, and sustainability.
Requirements
- 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.
- 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).
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.
Other
- PhD (preferred) or equivalent research/industry experience in Computer Science, Machine Learning, AI, Engineering, Materials Science or related fields.
- Publications in top ML/AI conferences (NeurIPS, ICML, ICLR, ACL).
