Founding 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
• Partner with product and engineering to turn customer feedback into roadmap inputs
• Support technical discussions, demos, and deployments across the sales cycle

Week in the Life

• Join customer calls to scope scientific workflows
• Deploy and test models on real customer datasets
• Work across infrastructure, APIs, and ML systems to ensure performance
• Iterate quickly based on feedback from scientists
• Translate field learnings into product improvements

Ideal Qualifications

• Strong engineering fundamentals (Python preferred)
• Experience working with AI/ML systems or data pipelines
• Ability to operate in ambiguous, fast-moving environments
• Strong communication skills - able to interface with both technical and non-technical stakeholders
• Willingness to work onsite in San Francisco

Technology

Tamarind operates at the intersection of DevOps, MLOps, and Computational Biology. You’ll work across:

• ML models (protein design, structure prediction, docking)
• GPU-based compute infrastructure
• APIs, workflows, and orchestration layers
• Scientific datasets and research pipelines