---
title: 'Member of Technical Staff, Product Engineering at San Francisco Tensor Company'
canonical: 'https://feeny.ai/job/member-of-technical-staff-product-engineering-san-francisco-tensor-company-san-41h1c6445kr8'
type: 'job'
last_seen: '2026-09-13'
---

# Member of Technical Staff, Product Engineering at San Francisco Tensor Company

- **Company:** San Francisco Tensor Company
- **Location:** San Francisco, CA
- **Compensation:** $225k–$275k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-08-29
- **Last confirmed live:** 2026-09-13
- **Apply:** https://jobs.ashbyhq.com/sf-tensor/217d8c39-cd29-4c84-8491-f559197a25da

## Job description

At SF Tensor, we're building the future of high-performance compute We firmly believe that the future of AI depends on the unglamorous: rethinking and rebuilding the stack, all the way down. From the hardware underneath it to the compiler targeting it and the cloud running it. Right now those three things fight each other and that friction shows up as a tax on every researcher trying to build something ambitious. We're here to axe that tax and make compute faster, cheaper and more available. When we succeed, compute will be portable enough that "which cloud, which chip" stop being something you worry about. To achieve this, we are building our Kernel Optimizer, which takes code and finds its fastest possible form for whatever vendor and cluster topology you point it at, automatically, as well as the Model Foundry which manages the runs, makes research easier and moves workloads across clouds and chips as prices and availability ship, instead of leaving you locked into whatever vendor you signed with first. We're backed by Susa Ventures, Y Combinator, along with some great funds and angels including Max Mullen and Paul Graham, as well as founders and executives at Neuralink, Notion and AMD. We're looking for researchers, engineers and organizations who agree with the basic premise: you don't get the next leap in AI without a leap in compute first.

## About the Role

We build the fastest GPU compiler in the world. Most compilers have to preserve correctness at every transform, constraining how far they can search, while we prove correctness at the end instead, allowing us to search a far wider space, with agents, with RL, with anything that works and still guarantee the result. It's why we hold #1 on NVIDIA's own kernel benchmark across hundreds of production kernels. Speed at the kernel layer is only worth what we do with it though, so we're hiring a Member of Technical Staff for Product Engineering to own the surface that the engine reaches the world through. That surface is Model Foundry and it takes a researcher from idea to a running experiment in one commit or one message. When a researcher needs to see the perplexity curve from last night's run or to try an experiment with a new annealing rate, Foundry writes the config, queues the job on whatever silicon makes sense that week, then stream the logs back and versions the whole thing so it can be reproduced six months later. You'll own it end to end from the interfaces researchers live in all day to the services behind them, making sure the software doesn't just work, but the experience feels right and is enjoyable even when using it 16 hours a day for months on end. The users are close and the loop is short. Foundry is used by both our customers, our internal research team as well as our forward-deployed researchers to train models every hour of the day, so your users either sit next to you or a Slack message away. The data you're processing is not gentle: you'll be processing telemetry from thousands of GPUs across NVIDIA, AMD, TPU and Trainium from runs that last for weeks to months parsing logs and charts that don't stop and making sure all of the data is accessible to user as well as their agents.

## What You'll Do

- You'll take full ownership of product areas in Model Foundry: scoping, designing, building and iterating on them
- You'll build clean, fast and polished web interfaces with NextJS that feel almost native
- You'll design the surfaces where researchers live: run dashboards, live log streaming, experiment comparison, eval results, cluster and storage views
- You'll build the conversational and commit-driven entry points into Foundry, where a Slack message or commit become a training run
- You'll obsess over the small things, from loading states to smooth animations and the edge cases that turn a good product into an exceptional one
- You'll work directly with our forward-deployed researcher and our own training team. They're your users, they sit next to you and their feedback loop is measured in minutes
- You'll make smart product tradeoffs with little hand-holding, which means knowing when to ship fast and when to spend extra time polishing

## What We're Looking For

- Someone with proven experience shipping complete products, ideally something you can show us
- Someone with real taste and a sharp eye for design and motion, which means you notice when an animation eases awkwardly, when padding feels inconsistent or something just feels cheap
- Someone with solid backend chips, which means you can build APIs and troubleshoot production issues
- Someone with enough infrastructure comfort to handle AWS, containers and Kubernetes (you don't need to be a DevOps wizard but shouldn't be scared of a Dockerfile either)

## Nice to Have

- Someone with experience working with Bun or an eagerness to dive in
- Someone with a background in developer tools, infrastructure products or building for technical users
- Someone with any exposure to ML training workflows or tools like Weights & Biases, Ray or Slurm
- Someone comfortable using Figma to mock up things yourself when needed

## Why Join Us

Most infrastructure companies treat their interface as an afterthought and their users can tell. The engineering and engine behind our product is as good as it gets and the product on top deserves to be just as good. You'll be defining what researchers at frontier labs feel when they train a model and are staring at logs and curves for hours on end with the goal of making the research experience as enjoyable as possible and let the infrastructure get out of the way and fade into the background. We're a small team operating at frontier scale. We pre-trained foundation models on 4,000 AMD GPUs as a team of three, designed and brought up GB300 NVL72 clusters and designed a TOP500 supercomputer. We believe that hard problems get solved in person and most of our work happens at our office in San Francisco. We offer relocation assistance and, where possible, we'd like you here as often as possible. The base salary range for this full-time position is $225,000-$275,000, plus meaningful equity and benefits.

## About San Francisco Tensor Company

## Company Overview
- **One-liner**: San Francisco Tensor Company (SF Tensor) builds an integrated stack of a programming language (Emma), an automatic kernel optimizer, and a cross-cloud compute platform (Tensor Cloud) to make AI and HPC workloads faster, cheaper, and hardware-portable.
- **Entity Type**: Private (Startup, Y Combinator Fall 2025 batch)
- **Headquarters**: San Francisco, California, USA
- **Founded**: 2025
- **Founders**: Ben Koska (CEO), Tom Koska, Luk Koska

## Core Business
- **Primary industry/industries**: AI Infrastructure, High-Performance Computing (HPC), Compiler & Kernel Optimization, Cloud Compute
- **Target customers**: AI research labs, AI startups, and enterprises training or running large-scale machine learning models (B2B).
- **Mission or purpose statement**: To reinvent how the world computes by making computation faster, cheaper, and more portable across every platform, thereby breaking the hardware vendor lock-in (e.g., NVIDIA's CUDA moat) and allowing researchers to focus on advancing models rather than managing infrastructure.

## Products & Services
- **[Emma Language]**: A new programming language designed to abstract away the quirks of individual hardware platforms (GPUs, TPUs, etc.) while delivering performance equivalent to hand-tuned kernels. Allows code to be written once and run on any hardware without rewriting.
- **[Kernel Optimizer]**: An automatic optimization engine that transforms training kernels into their mathematically fastest forms by simulating memory, cache, and hardware topology. It often exceeds the performance of hand-tuned human code. Available as a service.
- **[Tensor Cloud]**: A managed, cross-cloud compute platform that automatically finds the cheapest hardware (GPUs) across all major providers. It orchestrates training jobs, handles spot instance preemption, and manages infrastructure complexity for teams running from 1 to 10,000 GPUs. Cuts compute costs by up to 80%.

## Market Standing
- **Valuation/Market Cap**: Not publicly available (early-stage startup).
- **Key Metric**: Total Funding – Backed by Y Combinator (Fall 2025 batch). Specific funding amount not disclosed. Revenue is not publicly available.
- **Notable Investors/Partners**: Y Combinator (Primary Partner: Harj Taggar).
- **Growth Signals**: The company was founded in 2025 and is already live with Tensor Cloud, a Kernel Optimizer, and the Emma language preview. It has a team of 6 people and is actively hiring for multiple founding-level engineering roles, indicating rapid early-stage scaling.

## Competitive Advantages
- **Integrated Full Stack**: By combining a purpose-built language (Emma), a hardware-aware kernel optimizer, and a cross-cloud orchestration platform, they offer a unified solution that competitors typically only address piecemeal.
- **Hardware Agnosticism**: Their core mission is to break the CUDA moat, making it easy for AI teams to run on AMD, Google, or Amazon hardware without performance loss, thereby reducing costs and dependency on NVIDIA.
- **Automatic Optimization**: Their kernel optimizer claims to beat hand-tuned implementations by using algorithmic reformulation based on micro-benchmarking of hardware topology, a deep technical moat.
- **Founding Team**: Founders with deep technical backgrounds (Ben Koska finished a BSc in CS at age 16) and a clear, ambitious vision.

## Strategic Focus
- **Product Development**: Continuing to build out the Emma language, the Kernel Optimizer, and Tensor Cloud into a seamless, production-ready platform.
- **Market Adoption**: Onboarding early design partners and AI labs to validate the stack and build a user base.
- **Hiring**: Aggressively hiring founding engineers (GPU Kernel, Compiler, Research, Product) to scale the team and accelerate development.
- **Breaking Vendor Lock-in**: A core strategic goal is to provide a viable alternative to NVIDIA's CUDA ecosystem, enabling customers to leverage cheaper, non-NVIDIA hardware.

## Why Work Here
- **Mission-Driven**: Opportunity to solve one of the hardest and most impactful problems in AI: the compute bottleneck and infrastructure complexity.
- **Technical Depth**: The work involves pushing hardware to its limits, designing new programming languages, and writing cutting-edge compilers and kernel code. The culture values deep technical engagement.
- **Founding Team Impact**: As a very early-stage company (6 people), new hires will be founding engineers with significant ownership, equity (1% - 2% range for some roles), and influence over the product and culture.
- **Compensation**: Offers competitive salary in the 90th percentile+ (e.g., $225k-$315k base for some roles), plus meaningful equity.
- **Benefits**: Premium health, dental, and vision insurance for employee and family; unlimited PTO; generous parental leave; 401K with salary matching; team offsites and retreats.
- **Work Environment**: Described as a team of builders, mathematicians, and systems thinkers. They emphasize moving fast without cutting corners. The hiring process is rigorous and transparent.
- **Location**: Based in San Francisco, CA. Roles are listed as on-site.

## Sources
1. [sf-tensor.com](https://sf-tensor.com/)
2. [sf-tensor.com/careers](https://sf-tensor.com/careers)
3. [ycombinator.com](https://www.ycombinator.com/companies/sf-tensor)
4. [sf-tensor.com/news](https://sf-tensor.com/news/introducing-sf-tensor)
5. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/sf-tensor)

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