Founding Forward Deployed Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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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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Application

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Assessment

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Deadline

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About this role

Founding Forward Deployed Engineer 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. FORWARD DEPLOYED ENGINEER ABOUT THE ROLE We're hiring a Forward Deployed Engineer - one of the highest-leverage roles on the team. You'll sit at the intersection of engineering, product, and customers - working directly with scientists, ML teams, and pharma stakeholders to deploy Tamarind into real-world workflows. This role owns the full arc: from first technical conversation → pilot → production deployment. Every deployment becomes a product signal, a reference customer, and a revenue driver. The customer relationship moves at the speed you move. WHAT YOU'LL DO - Work directly with customers (scientists, ML teams, pharma orgs) to understand workflows and translate needs into deployable solutions - Stand up AI/ML workflows using Tamarind's platform - often within days of initial engagement - Configure and deploy models (e.g. protein structure, docking, generative models) against real datasets - Own pilots end-to-end - from scoping to execution to expansion - Debug, adapt, and optimize workflows across compute, models, and data pipelines

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