Research Scientist Intern

Job Description:

• You will work with the team to conduct cutting-edge AI and computational biology research. Your primary tasks will include improving existing models and exploring new methodologies to advance our AI capabilities in biology.
• You will work with the team on designing and executing experiments, analyzing complex datasets, and applying statistical techniques to validate the performance and robustness of AI systems.
• Additionally, you will collaborate closely with the AI/ML researchers and computational biologists on the team to develop our state-of-the-art AI for biology foundation models.

Qualification:

• M.S. or Ph.D. student (or evidence of equivalent level of expertise) in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.
• Skilled in developing, implementing, and debugging deep learning methods/models in popular frameworks, such as JAX, TensorFlow, or PyTorch, with an interest in generative models, graph neural networks, or large-scale deep learning applications.
• Strong theoretical foundation (e.g., statistics, optimization, graph theory, linear algebra).
• Passion for interdisciplinary research (emphasizing the intersection of AI and Biology), and willingness to acquire necessary domain knowledge.
• Motivated and self-driven with the ability to operate with partial descriptions of high-level objectives (as is typical in a start-up environment).
• Familiarity with software engineering best practices (version control, documentation, etc).

Nice to Have:

• 3 year PhD student and above.
• Proven track record in research and innovation demonstrated through contributions in top-tier AI/ML (e.g., NeurIPS, ICML, CVPR, ECCV, ICCV, ICLR) and/or core biology (e.g., Nature, Science, or Cell) journals and conferences.
• Intern experience in industry (e.g., OpenAI, FAIR, Deepmind, Google Research).
• Hands-on experience working at the intersection of AI and Biology.
• Experience in large-scale distributed training and inference.
• Open-source contributions, especially if used by others.