
Tamarind Bio provides scientists a no-code platform to design and optimize proteins, antibodies, enzymes, and peptides at large scale. It exposes web interfaces and APIs that run public…

Tamarind Bio provides scientists a no-code platform to design and optimize proteins, antibodies, enzymes, and peptides at large scale. It exposes web interfaces and APIs that run public…
What they do: No-code SaaS platform and API for large-scale computational protein design (AlphaFold, RFdiffusion, ProteinMPNN, GROMACS and 200+ models).
Customers: Life-science companies, biotechs, pharmaceutical researchers, and academic labs.
Funding: $13.6M total (includes $12M Series A led by Dimension Capital); prior Seed with Y Combinator participation.
Founded / HQ: Founded 2023; headquartered in San Francisco, California.
Computational protein design, antibody/peptide/enzyme engineering, structure prediction and high-throughput molecular-design workflows.
2023
Biotechnology
Crunchbase lists a Seed round on Apr 3, 2024 with Y Combinator and other investors.
$12,000,000
Company announced a $13.6M fundraise including a $12M Series A led by Dimension Capital.
“Participation from Y Combinator; Series A led by Dimension Capital with participation from other institutional investors (e.g., Eight Capital, Treeo VC listed as investors).”
We’re hiring 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.
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Technology
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.