Computational Scientist
Curates, builds, and scales AI-powered drug discovery tools including structure prediction, protein design, and docking models. Collaborates with customers to troubleshoot and optimize biological AI workflows using Python, PyTorch, and cloud infrastructure.
About the job
Responsibilities
- Work with founders and engineers to integrate and deploy biological ML models on the Tamarind platform.
- Build and refine workflows connecting tools like structure prediction, docking, and scoring models.
- Partner with customers to troubleshoot pipelines and help them run large-scale discovery workflows.
- Evaluate new research tools and integrate promising models into the platform.
- Contribute to improving reliability, performance, and scalability of scientific pipelines.
Qualifications
- Strong background in computational biology, computational chemistry, bioinformatics, or related field.
- Familiarity with ML and physics-based tools in structural biology, molecular dynamics, protein–ligand docking, or virtual screening.
- Experience working with biological data such as molecular structures, compounds, sequences, and databases.
- Programming experience in Python and scientific computing workflows.
- Comfort working with cloud infrastructure and ML tooling (AWS, Docker, CUDA, Conda, PyTorch, TensorFlow).
- Located in the SF Bay Area or able to relocate.
Skills
Python, PyTorch, TensorFlow, CUDA, Docker, AWS, Conda, Molecular Modeling, Protein Design, Structural Biology, Machine Learning, Protein-Ligand Docking, Molecular Dynamics
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