Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Provides hands-on technical leadership across architecture, infrastructure, backend services, APIs, ML systems, and product development. Partners with the CTO on technical direction, leads design and code reviews, mentors engineers, improves reliability and scalability, and works with customers to translate requirements into solutions. The role requires substantial production software engineering experience, cloud infrastructure expertise, and the ability to guide a growing engineering team without direct management responsibility.

Build and scale Tamarind Bio’s computational biology platform, including infrastructure, APIs, web interfaces, ML services, and deployment systems. Partner with founders and customers to define products, ship features, operate reliable services, and support AI-powered drug discovery workflows. Responsibilities span AWS DevOps, MLOps, frontend development, HPC scaling, model deployment, architecture, coding, design reviews, and practical production operations.

Deploy AI/ML drug-discovery workflows for scientists, ML teams, and pharma customers. The engineer scopes customer needs, configures models and infrastructure, runs pilots, debugs data and compute pipelines, supports technical demos, and translates field feedback into product improvements and roadmap inputs.

Founding Product Marketer responsible for establishing Tamarind Bio’s marketing foundation, including brand, positioning, messaging, product launches, GTM campaigns, content, case studies, thought leadership, conferences, and demand generation. The role translates complex AI, infrastructure, and computational biology concepts into compelling narratives for scientists, executives, and enterprise buyers while partnering closely with founders, customers, sales, engineering, and product teams.

Build Tamarind Bio’s outbound sales engine as its first GTM hire. Responsibilities include prospecting through scientific papers, conferences, research labs, and partnerships; designing multichannel campaigns; engaging scientists, ML researchers, and pharma stakeholders; qualifying enterprise leads; supporting demos, pilots, and deal cycles; and translating customer feedback into product and GTM improvements.

Design, build, and maintain infrastructure for scaling machine-learning inference across more than 150 biological models. Responsibilities include orchestrating containerized workloads with Kubernetes, optimizing resource allocation, supporting GPU workloads, ensuring high availability, and scaling AWS infrastructure. The role requires collaboration with founders and engineering teams in a fast-paced onsite startup environment, with involvement across DevOps, MLOps, cloud systems, automation, monitoring, and computational biology technologies.