Computational Scientist

Benefits

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

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Weekend work

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About this role

Computational Scientist San Francisco | OnSite 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 a Computational Scientist to help curate, build, and scale Tamarind's library of AI-powered drug discovery tools. In this role, you'll work closely with the founders and engineering team to operationalize cutting-edge models for structure prediction, protein design, docking, scoring, and other core biological AI workloads. You'll help transform fragmented research tools into production-ready workflows that scientists can run reliably at scale. You'll collaborate directly with customers to understand their discovery challenges and help them leverage Tamarind's platform to run real biological AI pipelines. This often involves chaining multiple tools together, troubleshooting workflows, and identifying opportunities to improve the platform. This role sits at the intersection of computational biology, machine learning, and scientific infrastructure, and is ideal for someone excited about applying the latest advances in AI to real-world drug discovery programs. Our techstack: - Python, PyTorch, TensorFlow, CUDA, Conda, Docker, AWS (EC2, S3, DynamoDB), molecular modeling tools, protein design frameworks, structural biology tooling, APIs

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