About Tamarind Bio

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.

💻 About the Role

We’re looking for two exceptional Fullstack Software Engineers (Contract) to help us deliver several large, high-impact projects through the end of January 2026. This is a hands-on, short-term role ideal for engineers who thrive in fast-paced environments and want to work onsite with a small, mission-driven team.

You’ll collaborate closely with our founders to design, build, and scale our infrastructure, APIs, and web interface. You’ll own key parts of our stack end-to-end — from architecture to deployment — and ship features that directly impact scientists and customers.

These roles are onsite in San Francisco (up to 40 hours per week). We’re looking for U.S.-based engineers who can jump in quickly and help accelerate delivery on short-term projects critical to our roadmap.

Tech Stack
Python, React, AWS (EC2, S3, DynamoDB), Docker, CUDA, Conda, TensorFlow/PyTorch; notebooks; bash/Slurm; APIs & web apps.

A Week in the Life

• Partner with founders on product decisions, scope experiments, and ship quickly

• Build and operate services (infra + ML + web) while iterating with customer feedback

• Mix of coding, design reviews, and practical deployment work

• Work onsite with the team in SF

Ideal Qualifications

• Fullstack experience across backend, infra, and frontend

• Strong proficiency in Python and React

• Experience with AWS DevOps (EC2, S3, DynamoDB, Docker) and/or MLOps (CUDA, TensorFlow, PyTorch, Conda)

• Adaptability to work on diverse problems (batch HPC scaling, frontend dev, automated fine-tuning, etc.)

• U.S.-based and available to work onsite in San Francisco through January

Nice to Have

• Experience with AI/ML model deployment across various architectures and environments

• Familiarity with computational biology or ML for scientific applications

Technology
Our technology sits at the intersection of DevOps, MLOps, and Computational Biology. We tackle challenges from scaling ML inference on AWS across hundreds of GPUs to parsing pdb files with Biopython. Our engineers work fluidly between Docker containers, Colab notebooks, bash scripts, and slurm jobs to support our growing customer base.