Product

TensorZero is building an automated AI engineer (”TensorZero Autopilot”) powered by our open-source LLMOps platform (”TensorZero Stack”).

TensorZero Stack

We started by building an open-source LLMOps platform for full-stack LLM engineering:

• Gateway: access every LLM provider through a unified API (<1ms p99 latency)

• Observability: monitor your LLM systems, programmatically or with a UI

• Optimization: optimize your prompts, models, and inference strategies

• Evaluations: benchmark individual inferences or end-to-end workflows

• Experimentation: deploy with built-in A/B testing, fallbacks, etc.

Today, the TensorZero Stack is used by companies ranging from frontier AI startups to Fortune 50 enterprises.

TensorZero Autopilot

Now we’re working on TensorZero Autopilot, an automated AI engineer powered by our open-source LLMOps platform. Think of it like “Claude Code for TensorZero”.

TensorZero Autopilot collaborates with engineering teams to automate LLM engineering — with full visibility and control. For example, it can:

• Analyze millions of inferences to surface error patterns and optimization opportunities

• Recommend models and inference strategies to improve quality, cost, and latency

• Generate and refine prompts based on human feedback, metrics, and evaluations

• Drive optimization workflows like fine-tuning, reinforcement learning, and distillation

• Set up evaluations, prevent regressions, and align LLM judges to real-world scenarios

• Run A/B tests to validate changes, identify winners, and close the feedback loop

By itself, TensorZero Autopilot drove substantial performance improvements for LLM systems in benchmarks and synthetic environments ranging from data extraction to customer support agents.

Role

We're looking for a 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, 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 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 4k+ citations and several first-author papers at NeurIPS and other top ML venues.

• Alan Mishler (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.