We enable any scientist to access AI-powered drug discovery. Thousands of scientists from large pharma companies, top biotechs, and academic institutions use Tamarind to design protein drugs, improve industrial enzymes, and create cutting edge molecules that weren’t feasible until now.

New AI models are quickly eclipsing physics-based tools in computational drug discovery. Scientists often struggle to fine-tune, deploy, and scale these models, leaving breakthroughs on the table. Tamarind provides a simple interface to the vast array of tools being released daily.

We’re looking for a computational biologist to help us curate, build, and scale a comprehensive set of drug discovery tools. You will work directly with the founders to build and deploy tools for structure prediction, protein design, docking, scoring, and more. You’ll be working directly with customers to help them leverage the best open source software for their task, often chaining multiple tools together. We’re looking for someone excited about the latest advancements in AI drug discovery and both breadth and depth of knowledge in the field. 

Qualifications and Skills

• Knowledge of ML and physics based tools in structural biology, protein design, molecular dynamics, protein-protein and protein-ligand docking, peptide discovery, virtual screening, etc.
• Knowledge of enzymology, protein therapeutics, and peptide discovery.
• Familiarity with literature and evolving state of the art in computational protein engineering tools
• AWS DevOps (DynamoDB, EC2, S3, docker, etc.) and MLOps (CUDA, conda, TensorFlow, PyTorch)
• Ability to develop general purpose, production-ready software
• Located in the SF Bay Area or able to relocate to the Bay Area

Pluses:

• Experience developing machine learning models for proteins (language models, structure prediction, design)
• Knowledge of full stack software engineering, e.g. React and API development
• Computational chemistry, including molecular dynamics, docking, and virtual screening methodologies for small molecule discovery
• Graduate degree in math, CS, stats, bioengineering, comp bio, or a related field

Our technology sits at the intersection of DevOps, MLOps, and Computational Biology. We deal with problems ranging from scaling ML inference on AWS for hundreds of GPUs to dissecting pdb files with Biopython. We deploy a wide range of open source ML models for customers, navigating between Docker containers, Colab notebooks, bash scripts, slurm jobs, and more.