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About the Role

We are building the fastest GPU compiler in the world and are looking for a Member of Technical Staff for Product Engineering to own the user-facing surface of our Model Foundry product. You will be responsible for the end-to-end development of interfaces and services that enable researchers to go from idea to running experiment seamlessly. The role involves ensuring a high-quality, enjoyable user experience for researchers who spend long hours training models.

Responsibilities

Take full ownership of product areas in Model Foundry: scoping, designing, building and iterating on them
Build clean, fast and polished web interfaces with NextJS that feel almost native
Design the surfaces where researchers live: run dashboards, live log streaming, experiment comparison, eval results, cluster and storage views
Build the conversational and commit-driven entry points into Foundry, where a Slack message or commit become a training run
Obsess over the small things, from loading states to smooth animations and the edge cases that turn a good product into an exceptional one
Work directly with our forward-deployed researcher and our own training team
Make smart product tradeoffs with little hand-holding, knowing when to ship fast and when to spend extra time polishing

Requirements

Proven experience shipping complete products
Real taste and a sharp eye for design and motion
Solid backend skills to build APIs and troubleshoot production issues
Infrastructure comfort to handle AWS, containers and Kubernetes

Skills

NextJS
AWS
Containers
Kubernetes
Bun
Figma
ML training workflows
Weights & Biases
Ray
Slurm

Location

San Francisco

Work Type

Full-time
Onsite

Experience Level

Member of Technical Staff

Salary/Compensations

$225,000-$275,000

Benefits

Meaningful equity
Relocation assistance

About the Company

SF Tensor is building the future of high-performance compute for AI by rethinking and rebuilding the stack from hardware to cloud. We are developing a Kernel Optimizer and a Model Foundry to make compute faster, cheaper, and more available. We are backed by Susa Ventures, Y Combinator, and other notable investors and industry leaders. We are a small team operating at frontier scale, with experience in pre-training foundation models on large GPU clusters and designing supercomputers.