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.

We’re looking for an exceptional Founding SDR as our first GTM hire to scale our founder-led sales process for our enterprise customers.

We’ve grown from 0 to 7 figure revenue in one year, with just the founders, going live with many of the top 20 pharma, and raising 14M.

You’ll work directly with our CEO to develop and execute processes for generating and qualifying leads, managing outreach campaigns, engaging prospects throughout the pipeline, and potentially conducting field sales at industry conferences. 

What you’ll do:

• Create pipeline in creative ways, e.g. conference attendees, scientific paper authors, tracking scientific partnerships 
• Build outbound campaigns (email, LinkedIn, etc.) using tools like Apollo and Clays
• Engage qualified leads via gotten through content, conferences, LinkedIn
• Navigate sales cycles, procurement processes with prospective customers ranging from large biotechs to top 5 pharma
• Work with engineering team to scope projects based on customer feedback and showcase product capabilities
• Attend and present at drug discovery conferences

Requirements: 

• Previous experience in a sales role, selling a product at 5-7 figure ACV preferably SaaS or at a startup
• Willingness to travel
• Be based in person in San Francisco
• Desire to learn more about drug discovery

Nice to haves:

• Experience in life sciences
• Experience in selling complex technical products
• Automation/technical skills

Our technology sits at the intersection of DevOps, MLOps, and Computational Biology. We deal with problems ranging from scaling ML inference on AWS for hundreds of GPUs to dissecting pdb files with Biopython. We deploy a wide range of open source ML models for customers, navigating between Docker containers, Colab notebooks, bash scripts, slurm jobs, and more.