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Tamarind Bio

No-code, API-based access to bioinformatics tools

Founding Forward Deployed Engineer

Implementation & Technical Account ManagementConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USANo salary listedIn-person

About the Job

Requirements

  • Strong engineering fundamentals; Python is preferred.
  • Experience working with artificial intelligence and machine-learning systems or data pipelines.
  • Ability to operate in ambiguous, fast-moving environments.
  • Ability to interface with technical and non-technical stakeholders.
  • Willingness to work onsite in San Francisco.

Responsibilities

  • Work directly with customers, including scientists, machine-learning teams, and pharmaceutical organizations, to understand workflows and translate needs into deployable solutions.
  • Stand up artificial intelligence and machine-learning workflows using Tamarind’s platform, often within days of initial engagement.
  • Configure and deploy models, including protein structure, docking, and 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.
  • Partner with product and engineering teams to turn customer feedback into roadmap inputs.
  • Support technical discussions, demonstrations, and deployments across the sales cycle.
  • Join customer calls to scope scientific workflows.
  • Deploy and test models on real customer datasets.
  • Work across infrastructure, application programming interfaces, and machine-learning systems to ensure performance.
  • Iterate quickly based on feedback from scientists.
  • Translate field learnings into product improvements.

Desired Qualifications

  • Python.

About the Company

Tamarind Bio

No-code, API-based access to bioinformatics tools

Tamarind Bio provides a no-code and API-based platform that gives life scientists access to over 250 computational biology tools and AI models for protein design, structure prediction, and molecular docking. The platform is web-based with a scalable API and runs on AWS, so researchers can run large-scale experiments without writing code or managing high-performance computing infrastructure. It unifies tools like AlphaFold, RFdiffusion, and DiffDock under a single interface and handles GPU orchestration and parallelization. The goal is to speed up drug discovery by making advanced computational models accessible to academics and industry, while offering tiered SaaS plans and hosting custom models.

Company size
11-50
Company stage
Series A
Total funding
$13.7M
Headquarters
San Francisco, CA
Founded
2023

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