Research Scientists & Engineers - Generative AI for Biology

EPM Scientific-posted 10 months ago
Full-time • Entry Level
Palo Alto, CA
Administrative and Support Services
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We are partnered with a newly established startup where visionary scientists, engineers, and entrepreneurs are redefining the future of biology and medicine through the power of Generative AI. We are building a transformative, interdisciplinary R&D team dedicated to decoding biology holistically and enabling the next generation of life-changing solutions and hiring for individuals at all levels (preference Staff / Principal).

  • Design, implement, and benchmark novel deep learning methods for a range of biological applications.
  • Build, test, and deploy scalable, cloud-based pipelines that drive robust model training, testing, and inference.
  • Work closely with a diverse team of experts in AI, computational biology, and traditional biological sciences to integrate cutting-edge techniques with complex datasets.
  • Contribute to high-impact research by publishing findings in top-tier AI/ML and biology journals and conferences.
  • Tackle high-level challenges in drug design, bio-engineering, personalized medicine, and fundamental biomedical research.
  • PhD (or evidence of equivalent expertise) in Computer Science, Artificial Intelligence, Machine Learning, Computational Biology, Bioinformatics, Biochemistry, or a related technical field.
  • A strong track record in research and innovation, with contributions to top-tier AI/ML and/or core biology publications.
  • Proficiency in deep learning frameworks (JAX, TensorFlow, or PyTorch) and programming languages (Python, C/C++, Java).
  • A solid foundation in statistics, optimization, graph algorithms, and linear algebra.
  • Demonstrated experience in developing, implementing, and debugging complex models.
  • A drive for bridging AI and biology, with the ability to rapidly learn new domain knowledge.
  • Familiarity with version control, documentation, and open-source contributions.
  • 3+ years of post-PhD industry or postdoctoral experience, ideally in a startup or top-tier research lab environment.
  • Experience working with diverse biological datasets (e.g., transcriptomics, epigenetics, proteomics, genetics).
  • Familiarity with biological structure prediction algorithms (such as AlphaFold, RosettaFold) and network/systems biology methodologies.
  • Expertise in distributed training, ML on accelerators, and cloud services.
  • Prior exposure to graph ML frameworks (e.g., PyTorch Geometric, DGL) and advanced generative models.
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