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JOIN OUR TEAM AS A DEEP LEARNING ENGINEER
At Neoclease, we are developing novel nucleases tailored for specific diseases or genes of interest. Our approach leverages the power of synthetic biology and generative AI to create gene editing enzymes with unprecedented specificity, efficacy, and targeted delivery, setting a new standard in precision medicine. As a deep learning engineer, you will play a key role in developing and improving the machine learning algorithms that power our gene editing platform. Join us in our mission to revolutionize precision medicine.
LOCATION
San Francisco Bay Area
EMPLOYMENT TYPE
Full-time
What You'll Do
- You will work closely with our team of biologists, chemists, and bioengineers to develop and improve the machine learning algorithms that power our gene editing platform. This will involve applying deep learning techniques to large-scale genomics datasets, as well as designing and implementing new algorithms to improve the accuracy and specificity of our nucleases. You will be responsible for maintaining and optimizing our existing machine learning pipeline, as well as exploring new approaches to improve our platform's performance.
- In addition to your work on our gene editing platform, you will also have the opportunity to contribute to our broader research efforts in the field of synthetic biology. You will collaborate with our team of scientists to design and execute experiments that explore the potential applications of our nucleases in a variety of contexts, from basic research to therapeutic development.
Who You Are
- You have a strong background in computer science, with a focus on machine learning and/or deep learning. You are familiar with common deep learning frameworks and libraries, such as TensorFlow, PyTorch, or Keras. You have experience working with large-scale genomics datasets, and are comfortable applying machine learning techniques to complex biological problems. You are a creative problem solver, with a passion for developing new algorithms and approaches to tackle challenging scientific problems.
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