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# Software Engineer

- **Company:** Tamarind Bio Inc.
- **Location:** San Francisco, CA, United States
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Cloud Computing, Computational Biology, Nvidia CUDA, Programming Tools, Distributed Systems, Job Scheduling, Python (Programming Language), Molecular Modeling, Graphics Processing Unit (GPU), Pytorch, Autoscaling, Concurrency, Backend, Agentic-AI, Kubernetes, Infrastructure Automation Frameworks, Machine Learning Operations, Model Inference, Docker
- **Published:** November 3, 2026
- **Apply:** https://startup.jobs/software-engineer-tamarind-bio-8134060

## About the Role

We care more about demonstrated ability, judgment, and learning speed than pedigree or years of experience., You don't need to match every item. We're especially interested in engineers with depth in one or more areas and the range to learn the others.

 * Backend and distributed systems: Python, APIs, queues, workflow engines, concurrency, and failure handling.
 * Cloud infrastructure: AWS, Kubernetes, Docker, infrastructure as code, autoscaling, observability, and production operations.
 * ML systems: PyTorch, CUDA, model serving, GPU performance, and reproducible inference workloads.
 * Product craftsmanship: Design intuitive APIs and interfaces with meticulous attention to detail, usability, and the overall customer experience.

## Description

Our work spans distributed systems, cloud infrastructure, developer tooling, and product engineering. We deal with scaling inference across tens of thousands of GPUs, building long-horizon asynchronous agents for protein design, and maintaining secure cloud environments for our customers' most confidential research data.

You'll own projects end to end: understand the problem and scope out requirements, make the architectural decisions, write and ship the code, and make sure the system works reliably in production. You'll work directly with Tamarind's founders, fellow engineers, computational biologists, and customers.

We don't have a separate team for every layer of the stack, and the most interesting problems rarely fit neatly into one category. You might spend the morning designing a distributed job scheduler and the afternoon optimizing GPU inference or reviewing agent traces. We expect engineers to find the highest-leverage problems and follow them through to a working solution.

What you might work on

 * Scale molecular AI inference. Build orchestration systems that schedule heterogeneous CPU and GPU workloads across Kubernetes, HPC clusters, and customer-controlled cloud environments, improving scheduling, throughput, reliability, and cost.
 * Productionize new scientific models. Turn research repositories and newly published models into secure, reliable services that scientists can use without managing the underlying infrastructure.
 * Build agents for molecular design. Develop the infrastructure and tools for agents that run multi-step design campaigns, execute asynchronous workflows, evaluate candidates, and make decisions based on intermediate results.
 * Make complex workflows composable. Design APIs and primitives that let scientists and AI agents combine hundreds of molecular modeling tools into reproducible pipelines.
 * Own problems across the stack. Trace failures through APIs, queues, containers, GPU kernels, and scientific outputs until you find the underlying cause., * Have built and shipped technically ambitious software, whether at work, through research, or on your own.
 * Take responsibility for outcomes rather than tasks. You care deeply whether the whole system delivers an excellent experience for the user, not just whether your component works in isolation.
 * Can reason about large complex systems and design robust architectures, while also diving deep into particular implementation details.
 * Thrive in ambiguity. You can take an open-ended problem, figure out what needs to happen, and make progress without a detailed playbook.
 * Learn unfamiliar tools and domains quickly. You don't need a background in computational biology.
 * Enjoy working directly with users and translating loosely defined problems into concrete technical solutions.
 * Care about reliability and security without losing the ability to move quickly.
 * Use modern AI coding tools aggressively and thoughtfully, while maintaining a high bar for understanding, review, and correctness.
 * Are based in the San Francisco Bay Area or willing to relocate.