Computational Scientist

Tamarind Bio; San Francisco (CA); On-site; USD 90,000 - 120,000; Full time

Job summary

Tamarind Bio is hiring a Computational Scientist in San Francisco. This role involves working with AI-powered drug discovery tools and integrating biological ML models into the company's platform. You will partner with customers to solve discovery challenges and enhance workflow reliability and performance. Ideal candidates will have a strong background in computational biology, programming experience in Python, and familiarity with cloud-based ML tools such as AWS, Docker, and TensorFlow.

Qualifications

• Strong background in computational biology, computational chemistry, or bioinformatics.
• Familiarity with ML and physics-based tools in structural biology.
• Experience with biological data like molecular structures and databases.
• Programming experience in Python and scientific computing workflows.
• Comfort with cloud infrastructure and ML tooling.

Responsibilities

• Integrate and deploy biological ML models on the Tamarind platform.
• Build workflows connecting structure prediction and docking models.
• Troubleshoot pipelines and assist customers with discovery workflows.
• Evaluate research tools for integration into the platform.

Skills

Computational biology

Bioinformatics

Machine learning

Python

AWS

Tools

PyTorch

TensorFlow

Docker

CUDA

Conda

Job description

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.

Tech Stack

• Python, PyTorch, TensorFlow, CUDA, Conda, Docker, AWS (EC2, S3, DynamoDB), molecular modeling tools, protein design frameworks, structural biology tooling, APIs and workflow orchestration.

Week in the Life

• Work with founders and engineers to integrate and deploy biological ML models on the Tamarind platform.
• Build and refine workflows connecting tools like structure prediction, docking, and scoring models.
• Partner with customers to troubleshoot pipelines and help them run large‑scale discovery workflows.
• Evaluate new research tools and integrate promising models into the platform
  Contribute to improving reliability, performance, and scalability of scientific pipelines

Qualification Requirements

• Strong background in computational biology, computational chemistry, bioinformatics, or related field.
• Familiarity with ML and physics‑based tools in structural biology, molecular dynamics, protein–ligand docking, or virtual screening.
• Experience working with biological data such as molecular structures, compounds, sequences, and databases.
• Programming experience in Python and scientific computing workflows.
• Comfort working with cloud infrastructure and ML tooling (AWS, Docker, CUDA, Conda, PyTorch, TensorFlow).
• Located in the SF Bay Area or able to relocate.