Offer summary

Qualifications:

Ph.D. in Computer Science, Machine Learning, Computational Chemistry, Bioinformatics, or a related field., Hands-on experience with graph neural networks, multi-task learning, and generative modeling in drug discovery., Proficiency in designing and implementing machine learning architectures in PyTorch., Strong problem-solving skills and a demonstrated ability to conduct independent research..

Key responsabilities:

Develop and implement novel deep learning models for molecular property prediction and optimization.

Design and execute experiments to evaluate generative models for chemically valid molecules.

Collaborate with cross-functional teams to integrate ML models into drug discovery pipelines.

Conduct analyses of model outputs to identify areas for improvement.

Job description

About Atomwise:

Atomwise is a TechBio company leveraging AI/ML to revolutionize small molecule drug discovery. The Atomwise team invented the use of deep learning for structure-based drug design; a core technology of Atomwise’s best-in-class AI discovery and optimization engine, which is differentiated by its ability to find and optimize novel chemical matter. The company’s belief is that structurally novel chemical matter increases the likelihood of developing first-in-class and best-in-class medicines that have the potential to transform patient care.

Atomwise has extensively tested its discovery and optimization engine, delivering hit ID success in over 230 academic and collaboration projects-to-date that cover a wide breadth of protein classes and numerous “hard-to-drug” targets. Atomwise is building a pipeline of small-molecule drug candidates with first-in-class and best-in-class potential in immunology.

The Role:

We seek a highly motivated and creative Machine Learning Researcher to join our growing team. You will be at the forefront of developing and applying advanced machine-learning models to accelerate the discovery of novel therapeutics. Your work will directly impact the development of innovative AI-driven drug discovery platforms. This role offers the opportunity to work on challenging problems at the intersection of machine learning, chemistry, and biology, with access to extensive datasets and state-of-the-art computational resources.

Responsibilities:

Develop and implement novel deep learning models for molecular property prediction, design and optimization, and virtual screening.
Design and execute experiments to evaluate the performance of generative models in generating chemically valid and biologically relevant molecules.
Collaborate with cross-functional teams of chemists, biologists, and software engineers to integrate ML models into drug discovery pipelines.
Conduct in-depth analyses of model outputs and performance metrics to identify areas for improvement.
Stay up-to-date with the latest advancements in machine learning and computational chemistry, and contribute to the scientific community through publications and presentations.
Contribute to the development of scalable and efficient machine learning infrastructure by productionizing research for at-scale use by drug discovery teams.
Explore and implement innovative techniques for multi-objective optimization, property prediction, and active learning within the context of generative modeling for drug design.

Qualifications:

Ph.D. in Computer Science, Machine Learning, Computational Chemistry, Bioinformatics, or a related field.
Hands-on experience with graph neural networks, multi-task learning, active learning strategies, multi-objective optimization, and generative modeling, in particular their application to problems in small molecule drug discovery.
Proficiency in designing and implementing novel machine learning architectures in PyTorch.
Strong problem-solving and analytical skills, with a demonstrated ability to conduct independent research.
Experience with collaborative software engineering and developing high quality, maintainable code in a large codebase.
Demonstrable communication and collaboration skills.
A strong publication record in relevant conferences and journals is highly desirable.
Experience with 3D generative modeling and knowledge of protein-ligand interactions is a plus.
Experience in optimizing models for large-scale training and inference is a plus.
Familiarity with cheminformatics tools and libraries (e.g., RDKit) is a plus.

Compensation & Benefits

Competitive salary, commensurate with experience
Stock compensation plan – you'll be an Atomwise co-owner
Platinum health, dental, and vision benefits for you and your dependents
401(k) retirement plan with generous company match (up to 4%)
Flexible paid time off (PTO), 13 paid holidays, and wellness breaks for employees to spend time with their loved ones and recharge
Health Savings and Flexible Spending Account options to help save money on healthcare, daycare, and commuting
Employee Assistance Program (EAP) and Pet Insurance
Funding for professional development and conference attendance
Flexible work schedule
Generous paid parental leave

Atomwise is an equal opportunity employer and strives to foster an inclusive workplace. We are a TechBio company leveraging AI/ML to revolutionize small-molecule drug discovery, 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.

The base salary for this position is $175,000- $260,000. Pay is determined by multiple factors including, but not limited to, a candidate's geographical location, experience and skills. We’re considering all levels of experience for this position.

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