
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 B2B platform for large-scale protein, antibody, enzyme, and peptide design using public molecular models and physics-based computation
Tech focus: Generative and prediction models (AlphaFold, RFdiffusion, ProteinMPNN and 200+ models) with web UI and APIs for high-throughput workflows
Customers: Life-science companies and research organizations (B2B SaaS)
Founded / HQ: Founded 2024; headquartered in San Francisco
Seed-stage investors include Y Combinator, Treeo VC, Eight Capital, Dimension Capital
Protein and antibody engineering; computational biology infrastructure for biotech R&D
2024
Biotechnology
13600000
Investors reported include Y Combinator, Treeo VC, Eight Capital, Dimension Capital
“Seed syndicate including startup accelerator and early-stage VCs”
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 hiring exceptional to help us scale the computational biology platform that powers our drug discovery pipeline.
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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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Week in the Life:
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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.
🧩 Our Interview Process
We keep our process focused, transparent, and designed to give both sides a clear sense of fit.