Deep Learning Scientist (Seattle)
Vilya is a computational biotechnology company creating a novel class of medicines to precisely target disease biology. We believe computational approaches are an integral, if not foundational, component of drug discovery and development. Our platform is built on ground-breaking research in advanced computational approaches and taps into uncharted chemical space to design new molecular structures not found in nature.
Our molecules open the door to a brand-new class of medicine with enhanced drug-like properties. We are harnessing the power of our platform to go after previously impossible targets in an array of indications. Vilya’s ultimate goal is to solve some of the most challenging unmet medical needs that exist today.
Our Vision: Harness a revolution in technology and biology to better human health
Our Mission: Build an independent, leading biotech company founded on intelligent drug design to cure the incurable
We are seeking a highly-motivated, creative, and knowledgeable Deep Learning Scientist to help build Vilya as a key member of the ML/DL Design team. The role is responsible for inventing, developing, and maintaining deep learning datasets, models, and frameworks that power Vilya’s platform to design new therapeutics.
- Design, develop, optimize, and maintain deep learning frameworks that support and power Vilya’s drug design pipeline for macrocycles.
- Collaborate and communicate with fellow experts in deep learning as well as the broader interdisciplinary Vilya team, understand the domain problems, and apply deep learning to solve challenges.
- Apply and implement state of the art deep learning techniques in related fields (e.g. geometric deep learning, protein deep learning, etc…) to continuously improve our approaches to drug design and development.
- Ph.D. (or equivalent industry experience) in computer science and/or mathematics
- Strong knowledge of linear algebra, calculus, statistics, and graph theory.
- Capable of applying a wide range of fundamental machine learning theories and methods to solve challenges, including active learning and generative modeling.
- Experience developing, deploying, and managing deep learning models and formatting large datasets for real-world problems.
- Clear mathematical understanding of the deep learning techniques relevant to the protein and small molecule deep learning field (e.g. Transformers, Graph Networks, equivariant neural networks, diffusion and flow-based models)
- Proficiency in the Python/Pytorch framework for developing deep learning models.
- Desire to continuously expand domain-specific knowledge.
- Ability to work well with an interdisciplinary team and to communicate complex scientific ideas to diverse audiences.
- 4+ years of hands-on experience in developing deep learning models.
- Developing and deploying deep networks at scale.
- Expertise in geometric deep learning and point cloud modeling.
- Expertise in transfer learning and parameter efficient fine tuning techniques.
- Experience performing deep learning on small molecule/protein structures and interactions.
- Exposure to concepts in drug discovery such as ADME / Tox / DMPK.
- Opportunity to work in a disruptive startup with a talented, experienced, and growing team of dedicated individuals
- 401(k) plan with employer matching for contributions
- Stock options
- Excellent medical, dental, and vision coverage
- Open, flexible vacation policy
- Support to attend professional conferences that are meaningful to your career growth
- Monthly cell phone stipend
- Commuter benefits
- Salary Range : $140,000 – $210,000