LILA has created the world's first Operating System for Science powered by Scientific Superintelligence™. Backed by General Catalyst.
About the role
Lila Sciences is building scientific superintelligence by pairing large-scale automated experimentation with frontier machine learning. Within Lila, the Life Science AI (LSAI) function researches automated reasoning on biological data, combining state-of-the-art ML with breakthrough biology. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Currently enrolled in an MS or PhD program in machine learning, computational biology, bioengineering, computer science, statistics or a related quantitative field
- Hands-on experience building and training ML models, with strong Python skills and experience with PyTorch, JAX or a similar framework
- Coursework or research experience with biological sequence data (DNA or RNA)
- Curiosity about nucleic acid biology, such as RNA biology, gene regulation or sequence-to-function relationships
- Available for a full-time, 6-month co-op term onsite in San Francisco
More about this role
Lila Sciences is building scientific superintelligence by pairing large-scale automated experimentation with frontier machine learning. Within Lila, the Life Science AI (LSAI) function researches automated reasoning on biological data, combining state-of-the-art ML with breakthrough biology.
This co-op sits with the nucleic acid design group in LSAI, which builds models and design strategies for RNA and DNA sequences. You'll join our Nucleic Acid Design team for a 6-month co-op focused on open-ended research questions that could shape how Lila designs nucleic acid sequences in the future.
You'll work at the intersection of machine learning, sequence modeling and nucleic acid biology, on problems such as UTR design, regulatory element design, and using large reasoning models for scientific discovery. You'll own a defined research project with mentorship from ML scientists, and you'll work closely with experimental scientists to connect your models to real biological data.
The ideal candidate is curious, comfortable with open-ended problems, and excited to learn both the ML and biology behind sequence-to-function modeling.
- Lead a scoped research project in nucleic acid sequence...
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