At Atomwise, we pioneered deep learning neural network approaches for structure-based small molecule drug discovery. We leverage those technologies in one of the largest applications of machine learning in the life sciences to find treatments for Alzheimer’s, cancer, diabetes, drug-resistant antibiotics and other diseases. We’ve partnered with 4 of the top-10 US pharma companies, raised over $170M from top VCs, and have 100+ diverse projects running concurrently.
We value a collaborative and transparent culture that fosters scientific and technical discussion. We strongly believe that data wins over opinions, and aim for as little dogma as possible in our decision making. Our team members have expertise in a wide range of disciplines--from computational chemistry and structural biology to cloud-native best practices--and we regularly have internal seminars open to anyone interested in learning about these topics.
About the role
We are looking for a cheminformatics scientist with strong experience in compound modeling and data for ADMET prediction to join our ml.cheminformatics team. The team is relatively small, so there are lots of opportunities for both career growth and substantial contribution towards our success.
You will-
- Be a key contributor to our machine learning algorithms for late-stage compound optimization.
- Identify data sources for ADMET data and write code to ingest these data sources with a focus on data correctness and scalability.
- Communicate well and work with other scientists and software engineers to build our late-stage optimization program.
- Help maintain Atomwise’s scientific presence with conference presentations and publications.
- Use your drug discovery background and insights to collaborate across the R+D organization on a diverse range of projects.
Required Qualifications
- Ph.D. in Chemistry, Physics or life sciences, with 3+ years of industry and/or post-doctoral experience.
- Strong experience in navigating the interplay between data quality, quantity, diversity, statistics and machine learning model development.
- Solid understanding of the principles of ADME/Tox in drug discovery and development.
- Solid understanding of ADME/Tox-related experimental assays and their interpretation.
- Strong coding skills in at least one high-level programming language (Python, R, Java, C++, etc)
- Ability to work with people from diverse disciplines within cross-functional teams
Preferred Qualifications
- Experience supporting drug discovery scientists in the later stages of compound optimization.
- Familiarity with latest advances in ligand-based modeling and applications: graph-convolutional neural networks, GANs, multi-task models, transformers, variational auto-encoders, few shot learning, active learning
- Experience with real-world evidence for drug development projects
Compensation & benefits
- Competitive salary, commensurate with experience
- Stock compensation plan – you’ll be an Atomwise co-owner
- Platinum health, dental, and vision benefits
- 401k with 4% match
- Flexible work schedule
- Generous parental leave
- Relaxed work environment
- Great colleagues
Atomwise is an equal opportunity employer and strives to foster an inclusive workplace. Our mission is to develop better medicines faster, and we know that we need a diverse team to develop medicines that serve diverse populations. Accordingly, Atomwise does not make any employment decisions (including but not limited to, hiring, compensation, and promotions) on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, veteran status, disability status, or any other characteristics protected by applicable federal, state, and local law.
We strongly encourage people of diverse backgrounds and perspectives to apply.
Atomwise
