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 an exceptional founding engineer to help us scale our computational biology tooling. You’ll be responsible for maintaining and expanding the core infrastructure that powers our drug discovery software. You will work directly with the founders to design, build, and scale our web interface and API products. 

Interacting directly with customers is a highly important component of this job. You will build products around customer needs, taking full ownership over serving their requests. 

Ideal Qualifications:

• Adaptability and openness to work on diverse problems (e.g. batch HPC scaling, frontend development, automated fine-tuning…)
• AWS DevOps (DynamoDB, EC2, S3, docker, etc.) and MLOps (CUDA, Conda, Tensorflow, PyTorch)
• Front-end development (React/Vercel)
• Familiarity with the landscape of computational biology tools is a plus. Willingness to learn is a necessity.
• Located in the SF Bay Area or able to relocate to the Bay Area

Pluses:

• Structural biology and computational protein design skills: AlphaFold, RFdiffusion, Rosetta, ProteinMPNN, etc.
• Understanding of foundation models (e.g. LLMs and Diffusion), preferably applied to biological data
• Cheminformatics, including molecular dynamics, docking, and virtual screening methodologies

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.