TensorZero is an open-source stack for industrial-grade LLM applications. It unifies an LLM gateway, observability, optimization, evaluations, and experimentation.

See our GitHub repository to learn more.

Our ultimate goal is to enable LLM applications to learn from real-world experience. The current offering is the first step towards that vision: it enables a feedback loop for optimizing LLM applications, turning production data into smarter, faster, and cheaper models.

There are engineering teams building with TensorZero in all sorts of industries: healthcare, finance, recruiting, developer tools, consumer, etc.

Case Study: Automating Code Changelogs at a Large Bank with LLMs

Our technical team includes a former Rust compiler maintainer, machine learning researchers (Stanford, CMU, Oxford, Columbia) with thousands of citations, and the chief product officer of a decacorn startup.

We’re backed by the same investors as leading open-source projects (e.g. ClickHouse, CockroachDB) and AI labs (e.g. OpenAI, Anthropic). We’re lucky to have years of runway, giving us the flexibility to fully focus on open source for now with an ambitious long-term vision.

Role

We're looking for a Founding Member of Technical Staff with a background that combines product and engineering. As the first product hire at TensorZero, you'll wear many hats and quickly grow with the company. Early on, the role will be especially focused on our developer community. From coding to content creation, you'll work on whatever it takes to drive adoption: demos, integrations & partnerships, documentation, videos, social media, events, and more. You're a "wartime product manager" who can think outside the box, with the technical background to scale your impact independently.

Team & Culture

We’re a small technical team based in NYC (in person). As an early contributor, you’ll work closely with us and have a significant impact on the project’s future and vision.

• Viraj Mehta (Co-Founder & CTO) is an ML researcher with deep expertise in reinforcement learning, generative modeling, and LLMs. He received a PhD from CMU with an emphasis on data-efficient RL for nuclear fusion and LLMs, and previously worked in machine learning at KKR and a fintech startup. He holds a BS in math and an MS in computer science from Stanford.

• Gabriel Bianconi (Co-Founder & CEO) was the chief product officer at Ondo Finance ($20B+ valuation) and previously spent years consulting on machine learning for companies ranging from early-stage tech startups to some of the largest financial firms. He holds BS and MS degrees in computer science from Stanford.

• Aaron Hill (MTS) is a back-end engineer with deep expertise in Rust. He became one of the maintainers of the Rust compiler… while still in college. Later, he worked on back-end infrastructure at AWS and Svix. He’s also an active contributor to many notable open-source Rust projects (e.g. Ruffle).

• Andrew Jesson (MTS) is an ML researcher with deep expertise in Bayesian ML, causal inference, RL, and LLMs. He recently completed a postdoc at Columbia and previously received a PhD from Oxford, during which he interned at Meta. He has 3.3k+ citations and several first-author papers at NeurIPS and other top ML venues.

• Alan Mishler (incoming MTS) is an ML researcher with a background in causal inference, sequential decision making, uncertainty quantification, and algorithmic fairness (1.2k+ citations). Previously, he was an AI Research Lead at JPMorgan AI Research and received a PhD in Statistics from CMU, during which he interned at Google and Box.

• Shuyang Li (incoming MTS) previously was a staff software engineer at Google focused on next-generation search infrastructure, LLM-based search, and many other specialized search products (local, travel, shopping, maps, enterprise, etc.). Before that, he worked on ML/analytics products at Palantir and graduated summa cum laude from Notre Dame.

• _____ You?

What We Offer

• Competitive compensation — We believe that great talent deserves great compensation (salary, equity, benefits), even at an early-stage startup.

• Open-source contributions — The vast majority of your wor