Computational Scientist at Tamarind Bio

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

07 Jun, 26

Salary

250000.0

Posted On

09 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, PyTorch, TensorFlow, CUDA, Conda, Docker, AWS, Molecular Modeling, Protein Design, Structural Biology Tooling, APIs, Workflow Orchestration, Computational Biology, Machine Learning, Scientific Infrastructure, Drug Discovery

Industry

Software Development

Description

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 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 🧩 Our Interview Process We keep our process focused, transparent, and designed to give both sides a clear sense of fit. 1. Recruiter Screen (15–30 minutes) — Virtual 2. Technical Interview (90 minutes) — Virtual 3. Onsite (1 day) — San Francisco

Responsibilities

The Computational Scientist will curate, build, and scale the library of AI-powered drug discovery tools, operationalizing cutting-edge models for structure prediction, protein design, and docking in collaboration with engineering and founders. This role involves chaining multiple tools together, troubleshooting workflows with customers, and transforming research tools into production-ready, scalable workflows.