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 RoleWe’re hiring three exceptional Founding Software Engineers to help us scale the computational biology platform that powers our drug discovery pipeline.
In this role, you’ll collaborate closely with the founders to design, build, and scale our infrastructure, APIs, and web interface. You’ll own major pieces of our stack end-to-end — from architecture to deployment — and ship features that directly impact scientists and customers.
You’ll be responsible for maintaining and expanding the core systems that underpin our computational biology tooling, ensuring reliability, scalability, and performance as we grow.
This is a deeply collaborative and customer-facing role. You’ll work directly with users to understand their needs, translate feedback into product improvements, and deliver elegant solutions that accelerate their research.
Techstack:
Python, React, AWS (EC2, S3, DynamoDB), Docker, CUDA, Conda, TensorFlow/PyTorch; notebooks; bash/Slurm; APIs & web apps.
Week in the Life:
Partner with founders on product decisions, scope experiments, and ship quickly
Build/operate services (infra + ML + web) and iterate with customer feedback
Mix of coding, design reviews, and practical deployment work
Onsite expectation: Team currently onsite in SF ~5 days/week.
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)
Willingness to learn about biology-ML models
Located in the SF Bay Area or able to relocate to the Bay Area
Pluses:
Experience with AI/ML models across various architectures and environments
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.
🧩 Our Interview ProcessWe keep our process focused, transparent, and designed to give both sides a clear sense of fit.
1. Recruiter Screen (15–30 minutes) — Virtual
Meet with our recruiter to dive into your background, interests, and what you’re looking for next. We’ll also walk you through the company, team, and role.
2. Technical Interview (90 minutes) — Virtual
Co-Founder Interview (30 minutes): A conversation with Deniz Kavi (CEO & Co-founder) about product, collaboration, and how you approach building in an early-stage environment.
Technical Deep Dive (60 minutes): A live coding and problem-solving session with Sherry Liu (CTO & Co-founder) or a member of our engineering team.
3. Onsite (1 day) — San Francisco
Spend a day with us working on a mini project and meeting the team.
Skills Required
- Adaptability and willingness to work across diverse technical problems, including HPC scaling, frontend development, and automated fine-tuning
- Experience with AWS DevOps, including DynamoDB, EC2, S3, and Docker
- Experience with MLOps technologies, including CUDA, Conda, TensorFlow, and PyTorch
- Front-end development experience with React and Vercel
- Willingness to learn about biology-ML models
- Located in the San Francisco Bay Area or able to relocate there
- Experience with AI/ML models across various architectures and environments
What We Do
Tamarind Bio provides an AI-powered computational biology platform and API that gives scientists a simple, scalable interface to drug-discovery tools. Its no-code web platform and API support protein structure prediction, protein and enzyme design, molecular docking, and molecule optimization using models such as AlphaFold. The company serves researchers at pharmaceutical companies, biotech firms, and academic institutions without requiring specialized computing infrastructure.
