- Company Name
- Valence Labs
- Job Title
- Machine Learning Research ScientistNew
- Job Description
-
**Job Title**
Machine Learning Research Scientist – Drug Discovery
**Role Summary**
Lead pioneering machine‑learning research to accelerate drug discovery. Own end‑to‑end research initiatives, from hypothesis to deployment, and collaborate with multidisciplinary teams of scientists and engineers. Deliver high‑impact publications and maintain active engagement with the broader ML research community.
**Expectations**
- Drive the research agenda for frontier ML applications in drug discovery, including generative models, multi‑omic representation learning, and atomistic/structural modeling.
- Produce reproducible, publishable results and contribute to open‑science initiatives.
- Mentor junior researchers and manage project timelines within a cross‑functional environment.
**Key Responsibilities**
- Design, implement, and evaluate novel neural network architectures for modeling biological, chemical, and physical systems.
- Lead long‑running technical projects: ideation, algorithm development, experimentation, benchmarking, and deployment with platform teams.
- Collaborate closely with wet‑lab scientists, software engineers, and other ML researchers to translate scientific challenges into machine‑learning solutions.
- Present findings internally and externally via talks, papers, blog posts, and conference presentations.
- Maintain code‑base quality, documentation, and reproducibility standards; contribute to open‑source releases.
- Engage with academic and industry communities, co‑authoring publications in top journals and conferences.
**Required Skills**
- Advanced expertise in machine‑learning techniques: generative modeling, representation learning, and physics‑informed neural networks.
- Strong programming and engineering skills; rapid prototyping in Python (TensorFlow/PyTorch).
- Experience with multi‑modal biological/chemical/physical data and atomistic simulations.
- Proven ability to lead interdisciplinary projects, manage timelines, and mentor collaborators.
- Excellent written and verbal communication for scientific outreach and collaboration.
- Familiarity with open‑science practices, reproducible research workflows, and community standards.
**Required Education & Certifications**
- Ph.D. or equivalent in machine learning, computational biology, chemistry, physics, or a closely related technical field.
- Demonstrated research record with first‑author or lead‑author publications in peer‑reviewed venues (e.g., NeurIPS, ICML, ICLR, Nature, Science, JACS, or ACS).
- Experience applying ML methods to real‑world drug‑discovery problems, including development of new neural network architectures or modeling techniques.