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Tamarind Bio is looking for a Staff Software Engineer in San Francisco to lead technical direction and engineering practices. This hands-on role involves mentoring a team and working closely with the CTO on architectural decisions.
The ideal candidate will have over 7 years of software engineering experience, strong in distributed systems, and cloud infrastructure, particularly with AWS. You will be an integral part of a growing startup environment.
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 hiring a Staff Software Engineer to provide technical leadership across our engineering team as we continue to scale the platform behind Tamarind.
Today, our engineering team consists primarily of early and mid-career engineers. As the complexity of our systems and customer requirements continues to grow, we're looking for someone who can help guide technical decision-making, mentor engineers, and drive architectural discussions across the organization.
This is not an engineering management role.
We're looking for a hands‑on technical leader who enjoys writing code, reviewing designs, solving difficult engineering problems, and helping other engineers grow. You'll work closely with the CTO to shape technical direction while remaining deeply involved in execution.
The role spans architecture, infrastructure, product development, code reviews, technical mentorship, and engineering best practices.
Our technology sits at the intersection of DevOps, MLOps, and Computational Biology. We support workloads ranging from large‑scale ML inference and model deployment to workflow orchestration across research, infrastructure, and scientific computing environments.
The role requires someone who enjoys moving between architecture, implementation, operations, and mentorship while helping a growing engineering team tackle increasingly complex technical challenges.