Location: Hybrid (onsite + remote flexibility)
Contract Duration: Full-time contract
Start Date: ASAP
Machine Learning Engineer – Molecular Design (Hybrid)
Are you passionate about using machine learning to solve real-world challenges in drug discovery? We’re seeking a skilled Machine Learning Engineer to support advanced research in molecular optimization and design. This role is part of a cutting-edge team dedicated to transforming how small and large molecule drugs are discovered using AI-driven methods.
About the Role
As a Machine Learning Engineer, you will play a key role in designing and deploying innovative machine learning workflows to support drug discovery efforts. You'll build tools for molecular property prediction, Bayesian optimization, and active learning, all aimed at accelerating the development of novel therapeutics.
You’ll work on real-world projects in close collaboration with interdisciplinary teams of computational scientists, chemists, and biologists. From optimizing existing pipelines to proposing new approaches, your work will directly contribute to the future of AI-powered molecular design.
What You’ll Do
- Design and implement ML workflows for small and large molecule design.
- Build pipelines for probabilistic molecular property prediction and Bayesian acquisition to support active learning approaches.
- Collaborate with researchers and drug discovery teams across various disciplines.
- Support generative modeling projects for de novo molecular design.
- Analyze experimental and computational datasets to guide therapeutic design strategies.
- Contribute to existing initiatives and propose novel ideas for new research directions.
What You Bring
- PhD in Computer Science, Computational Biology, Chemistry, Physics, Chemical Engineering, or related field — or Master’s with 3+ years of relevant industry experience.
- Hands-on experience with modern ML libraries such as PyTorch, PyTorch Lightning, and tools like Weights & Biases.
- Demonstrated ability to build production-ready ML systems.
- At least one high-impact first-author publication or equivalent scientific contribution.
- Excellent communication skills – able to work across scientific and technical domains.
Bonus Points For:
- Experience with molecular dynamics, cheminformatics (e.g., RDKit), or computational chemistry.
- Background in one or more of the following areas:
- Molecular property prediction
- Small molecule design
- De novo drug design
- Medicinal chemistry
- Bayesian optimization
- Geometric deep learning
- Self-supervised learning
- Statistical or probabilistic modeling
- A public portfolio (e.g., GitHub) showcasing ML or computational science projects.
Why Join?
This is a unique opportunity to work with a visionary team at the intersection of machine learning and therapeutic discovery. Your work will impact the development of next-generation treatments by pushing the boundaries of computational molecular design.
If you're excited to work on complex, high-impact problems in a collaborative and fast-paced research environment, we’d love to hear from you!
Apply now to join a mission-driven team pioneering the future of drug discovery through machine learning.
#WCH
Job Types: Full-time, Contract
Pay: $49.00 - $79.00 per hour
Expected hours: 40 per week
Benefits:
- 401(k)
- Dental insurance
- Health insurance
- Life insurance
- Vision insurance
Application Question(s):
- Experience with ML libraries
- Experience with PyTorch & PyTorch Lightning
Work Location: In person
