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 forward deployed engineer to support our growing customer base of drug discovery researchers, ranging from biotech to large pharma. You’ll be responsible for supporting customer workflows, identifying root causes of issues, and implementing fixes. You’ll work closely with the product team to identify key features based on customer requests.  

Interacting directly with customers is a highly important component of this job. You will build features 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…)
• Strong programming skills in a fast-paced environment
• Willing to learn computational biology (no experience required)
• Located in the SF Bay Area or able to relocate to the Bay Area

Pluses:

• Understanding of foundation models (e.g. LLMs and Diffusion)
• AWS DevOps (DynamoDB, EC2, S3, docker, etc.) and MLOps (CUDA, Conda, Tensorflow, PyTorch)
• Front-end development (React/Vercel)

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.