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).

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, particularly protein structure prediction, protein sequence/structure modeling, or molecular design.

Experience with biological structure prediction algorithms or models such as AlphaFold2/3 or RoseTTAFold.

Experience in generative modeling for biological structures and sequences, including diffusion models, flow matching, or related approaches.

Experience in large-scale distributed training and inference.

Open-source contributions, especially if used by others.