TensorZero enables a data and learning flywheel for optimizing LLM applications: a feedback loop that turns production metrics and human feedback into smarter, faster, and cheaper models and agents.

Today, we provide an open-source stack for building industrial-grade LLM applications that unifies an LLM gateway, observability, optimization, evaluation, and experimentation. You can take what you need, adopt incrementally, and complement with other tools. Over time, these components enable you to set up a principled feedback loop for your LLM application. The data you collect is tied to your KPIs, ports across model providers, and compounds into a competitive advantage for your business.

Our vision is to automate much of LLM engineering. We're laying the foundation for that with open-source TensorZero. For example, with our data model and end-to-end workflow, we will be able to proactively suggest new variants (e.g. a new fine-tuned model), backtest it on historical data (e.g. using diverse techniques from reinforcement learning), enable a gradual, live A/B test, and repeat the process. With a tool like this, engineers can focus on higher-level workflows — deciding what data goes in and out of these models, how to measure success, which behaviors to incentivize and disincentivize, and so on — and leave the low-level implementation details to an automated system. This is the future we see for LLM engineering as a discipline.

For more details, see:

• GitHub Repository

• Announcement: TensorZero Raises $7.3M Seed Round to Build an Open-Source Stack for Industrial-Grade LLM Applications

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

• Essay: Think of LLM Applications as POMDPs — Not Agents

• VentureBeat: TensorZero nabs $7.3M seed to solve the messy world of enterprise LLM development

Role

We are looking for a Member of Technical Staff who's hungry to learn and contribute across the stack. Early on, you’ll contribute across the stack to ship the highest-impact user-facing features (e.g. TensorZero UI, applications of TensorZero). You'll work alongside and learn from experts in front-end (e.g. co-creator of Radix UI, React Router, RemixJS), back-end (e.g. ex-maintainer of the Rust compiler), and ML (e.g. researchers with thousands of citations). The vast majority of your work will be open source.

Team & Culture

We’re a small, deeply 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 ha