Senior Scientist
6 days ago
We are excited to announce a job opportunity for a Senior Scientist to join our team at Seismic Therapeutic Inc, a cutting-edge biomedicines company that leverages machine learning, protein engineering, and immunology to develop novel therapeutics.
The successful candidate will have a strong background in machine learning, computational biology, and protein biochemistry, with experience in developing and applying AI-driven approaches to design novel proteins with therapeutic functions. If you're passionate about improving patient outcomes and working in a dynamic, entrepreneurial environment, we encourage you to apply.
As a key member of our team, you will be responsible for designing and training new machine learning models to optimize natural proteins for therapeutic properties and design synthetic proteins with novel functions. You will also collaborate with wet lab scientists to bridge the divide from in-silico designs to real-world applications.
Estimated salary: $180,000 - $220,000 per year. We offer a comprehensive benefits package, including health insurance, retirement savings, and opportunities for professional growth and development.
Key Responsibilities:
- Design and train new machine learning models to optimize natural proteins for therapeutic properties and design synthetic proteins with novel functions.
- Spearhead computational de novo antibody design efforts to create new therapeutics targeting challenging antigens.
- Integrate diverse external and internal data sources to enable detailed modulation and multi-objective optimization of protein properties.
- Work with a diverse team of protein scientists, immunologists, and clinical experts to tailor modeling efforts toward high-impact therapeutic applications.
Required Qualifications:
- PhD in Mathematics, Computational Biology, Computer Science, Statistics or a related field.
- 3+ years of industry experience with developing and applying machine learning methods to create new biological drugs.
- Hands-on experience with protein engineering, statistical genetics, and/or deep learning models such as variational autoencoders and graph neural networks.
- Demonstrated experience collaborating with wet lab scientists to generate and analyze real-world data in an applied setting.
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