Calico is seeking a machine learning engineer to design, develop, and productionize machine learning algorithms and software tools to model macromolecule sequences and structures. This person will work with a team of dedicated experimental biologists to design experiments to test, and iteratively refine machine learning predictions.
The ideal candidate should be familiar with state-of-the-art machine learning techniques, including, for example, language/sequence modeling, representation learning, generative models, and self-supervised learning techniques. Candidates should have experience implementing, extending, and debugging state of the art machine learning models, and have expertise in designing and building robust data infrastructure and tools. Candidates will need to be autonomous in learning and applying the latest methods from deep learning literature. Candidates must demonstrate a strong ability to communicate ideas and results through publications and presentations and be able to work cross-functionally to execute on complex projects.
- Ph.D. in Computer Science, Computational Biology, or related technical field, plus 2+ years industry experience OR M.S. in Computational Biology, Computer Science, or related technical field, plus 4+ years industry experience
- Experience in applying deep learning to model biological sequences and structures
- Expert in TensorFlow, PyTorch, and/or JAX
- Strong software engineering skills and substantial expertise in Python
- Track record of outstanding communication and collaboration in a cross-functional environment
- Strong analytical and quantitative skill
Nice to have:
- Domain expertise in computational biology, biochemistry or structural biology
- Public recognition in the field, e.g. via scientific publications or success in computer vision competitions
- Prior experience working with biologists