Lila Sciences
Lila Sciences is the worlds first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of boundless discovery by applying AI to every aspect of the scientific method, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at www.lila.ai
If this sounds like an environment youd love to work in, even if you only have some of the experience listed below, we encourage you to apply.
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. What Youll 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. 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). Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. #J-18808-Ljbffr
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 Youll 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. 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). Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. #J-18808-Ljbffr