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Founding Forward Deployed Engineer

Tamarind Bio

San Francisco, CAFull-timePosted 4mo agoStill listed 4 days ago

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At a glance

Compensation
No compensation found
Location
San Francisco, CA
Schedule
Full-time
Work Authorization
Not specified

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Job overview

Tamarind Bio is hiring a Founding Forward Deployed Engineer. Tamarind Bio is seeking a Founding Forward Deployed Engineer to work at the intersection of engineering, product, and customers. This role involves collaborating directly with scientists, ML teams, and pharma stakeholders to deploy Tamarind's AI-powered drug discovery platform into real-world workflows. The engineer will own the full deployment arc, from initial technical conversations to pilot programs and production deployments, contributing to product signals, customer references, and revenue.

Key focus areas include Work directly with customers to understand workflows and translate needs into deployable solutions, Stand up AI/ML workflows using Tamarind’s platform, and Configure and deploy models against real datasets.

Preferred (not required): Python, AI/ML Systems, Data Pipeline, and Communication.

Skills & qualifications

RequiredNice to have

Skills

PythonAI/ML SystemsData PipelineCommunicationML ModelsProtein DesignStructure PredictionDockingGPU-Based Compute InfrastructureAPIWorkflowsOrchestration LayersScientific DatasetsResearch Pipelines

Qualifications

Strong Engineering FundamentalsAbility to Operate in Ambiguous, Fast-Moving EnvironmentsWillingness to Work Onsite in San Francisco

Full job 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.

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

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