
**Patch Bio engineers DNA for gene therapy.** We build technology to make gene therapies safer and more effective, and able to address a broader range of diseases. To do that, we work at the interface of genomics, machine learning, and DNA synthesis. We are a seed-stage, venture-backed biotech startup based in New York City.
Role
You will be responsible for training ML models using data from our massively parallel assays in order to design large libraries of candidate DNA/RNA. Ideally, you have experience in quantitative genomics applications, including training neural networks to predict functional properties of sequences.
Responsibilities
- Develop our core ML engine for exploring sequence space and designing new candidate elements for large functional screens.
- Work with our wet lab scientists to ground your designs in experimental considerations.
- Process NGS data to quantify activity of individual library elements and train predictive models on DNA sequences.
- Curate relevant external datasets and integrate with our internal data.
Qualifications
- PhD in computational biology, genomics, biomedical engineering, computer science, or equivalent
- Solid training and expertise in genomics, as evidenced by experience with NGS data.
- Knowledge of and experience with machine learning, including neural network frameworks (e.g., PyTorch or Tensorflow) and state-of-the-art network architectures.
- Familiarity with nucleic acid design, including issues related to biochemistry (secondary structure, GC content, repetitiveness), and other factors that affect experimental concerns.
- Experience working successfully in collaborative, multidisciplinary teams.
- Desire to work in a rapidly growing company, and a fast-changing environment.
Desirable plus
- Knowledge of gene/protein expression regulation
- Design of MPRA
- Experience collaborating with wet lab scientists in experimental design