Research Scientist Intern

Headquartered in Silicon Valley, we are a newly established start-up where a collective of visionary scientists, engineers, and entrepreneurs are dedicated to transforming the landscape of biology and medicine through the power of generative AI. Our team comprises leading minds and innovators in AI and biological science, pushing the boundaries of what is possible. We are dreamers who reimagine a new paradigm for biology and medicine.

We are committed to decoding biology holistically and enabling the next generation of life-transforming solutions. As the first mover in pan-modal Large Biological Models (LBM), we are pioneering a new era of biomedicine, with our LBM training leading to ground-breaking advancements and a transformative approach to healthcare. Our robust R&D team and leadership in LLMs and generative AI position us at the forefront of this revolutionary field. With headquarters in Silicon Valley, California, and a branch office in Paris and Abu Dhabi, we are poised to make a global impact. Join us as we embark on this journey to redefine the future of biology and medicine through the transformative power of Generative AI.

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
  
  
  
  

Join us as we embark on this journey to redefine the future of biology and medicine.

We are an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. GenBio AI participates in the U.S. Department of Homeland Security’s E-Verify program to confirm the employment eligibility of all newly hired employees. For more information on E-Verify, please visit www.e-verify.gov.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

---