Miami, FL · AI software engineer · open to the right team

Joshua Lazoff

I build AI that shows its work - or says nothing.

Twenty years of production engineering for names you know - Goldman Sachs, the team behind MetaMask, Chainlink. Now I build maps of knowledge, and AI that shows its work: systems that name a real source for every answer, and stay silent when they cannot. Everything runs on hardware I own, measured by tests you can read and run yourself. I want to do this on the hardest problem you have.

Senior AI software engineer · knowledge graphs · grounded, cited generation · evaluation pipelines · self-hosted inference

Python · TypeScript · Next.js · Neo4j · Postgres · vector search & embeddings · Playwright · Ollama on a home GPU cluster · Cloudflare edge

What I build

Systems that have to show their work

One rule runs through everything: an answer arrives with the source it came from, or it does not arrive. Most AI work asks for trust; this publishes the tests. Everything below runs today, in public, on hardware I own.

Maps of knowledge · honest AI

A library that answers with its sources

A living map of an ancient library - every piece carrying its source, every author joined to the teachers who taught them. Ask it anything, and the answer arrives with a reference you can look up. Then you can go check it.

The map today: 157,143 pieces, including 17,147 teachings - every one carrying its source (counted from the live database, 2 Aug 2026; the query is published).

Live now as OpenRabbi - a speaking agent grounded in the graph, running from my house.

Under the hood: a Neo4j knowledge graph, retrieval that must land on a real source, and speech served from my own GPUs.

Evaluation · testing

Every citation verified, word for word

Every citation is verified word for word against the real text before anything ships, in written tests and again in live conversation. You can read the methodology, run the queries behind every number, and check any figure yourself.

Python evaluation pipelines, reproducible from the paper: the same queries that grade the system regenerate every figure.

Infrastructure · product

A fleet, run like production

Forty-nine live sites built, deployed and QA'd by pipelines I wrote, on a GPU cluster in my house. And Digital Twin Pro, the company I founded: a private AI appliance you buy once and own outright - the same grounding discipline, packaged for teams whose data cannot leave the building.

Playwright audits gate every deploy: contrast, meta, links and copy are measured on the live page, in both colour modes, before and after every ship.

Underneath all of it: machines in my own house. A system built to show you where its answers came from should be one you can open up and look inside. The personal research this grew out of is at /torah/.

The story

Twenty years, three acts

Every engineer's career is a story about what they were trusted with. Mine runs from the trading floor of a global bank, to the front door of a new financial world, to a rack of machines that answer questions with sources. The thread underneath it has its own page.

  1. Act I · Systems that are not allowed to fail
  2. Origins

    San Juan → Cornell

    The question that runs the career

    Puerto Rico, before cloud anything: if you wanted a computer that worked, somebody had to build it - and by fourteen, people were paying me to be that somebody. At Cornell I found the discipline built around my favorite question. Information science asks: how do you represent the real world inside a computer? Twenty years later, I am still answering it - at larger and larger scale.

  3. Engineering

    Goldman Sachs · summers 2010 - 2012

    Trust you earn by shipping

    The same trade-processing team took me back three summers running. I built the tools that check the firm's records against its own risk systems, and ran the mentoring program for the incoming interns while still being one. Teaching the next class while shipping for this one: a pattern that never stopped.

  4. Engineering

    The floor · 2013 - 2015

    Where correctness is the whole job

    Securities technology: the systems that double-check the firm's trades and file the reports the law requires. When your software sits between a bank and its regulators, correctness is not a virtue - it is the whole job. I built a query service shared firm-wide as internal open source, in the firm's own in-house language. And I helped stand up a round-the-clock operation, spanning dozens of teams, that catches errors before they become anyone's worst day.

  5. Act II · The leap, and the years in command
  6. June 2015

    Two names, one month

    I left the “perfect job” and incorporated two companies in the same month: LazoffTech for client engineering, and a second for the long research project underneath. The first has funded the second ever since. The client years taught the lesson every senior engineer eventually learns. Organizing the material is harder than writing the code - and the structure you choose for knowledge determines everything you can do with it later.

  7. September 2016

    A list called “Websites to make”

    One year into independence, I wrote myself a five-line plan. The first line was this website - “the main site where all the projects are listed.” Most of that list is now real. The first line is the page you are reading.

  8. Engineering

    ConsenSys · 2018 - 2024

    The front door to a new financial world

    I joined through the education arm, building the platform that taught developers an entirely new way to program. I rose to senior engineering manager, responsible for every website behind MetaMask and consensys.io - the pages millions of people walked through to enter an economy that had not existed a decade earlier. Running the web presence of software that guards people's money teaches you a professional level of paranoia. I recommend it.

  9. Engineering

    Chainlink · 2021 - 2024

    The company whose product is an oracle

    Chainlink builds the systems that tell blockchains the truth about the outside world - prices, events, facts. The industry's own name for them: oracles. I built the sites and tools its developers rely on - three of those years while still running the web at ConsenSys, both engagements flowing through the research company. My day job was helping machines report facts with sources. So, it turned out, was everything else.

  10. Act III · The machines arrive
  11. Engineering

    2023

    A machine in the house

    Everything the career taught - connecting information, making it findable, running systems that do not lose things - pointed at one place, and it was not a data center. It was a machine on my own shelf, running models I could open up and inspect, holding data that never leaves the building. I stopped renting my computing and started owning it.

  12. The rule that runs through everything

    An answer arrives with the source it came from - or it does not arrive.

    I found the rule on the oldest shelf in the world, and I wrote it into the code.

  13. Engineering

    2026

    The year of the machines

    When the AI tools matured, two decades of collecting became fuel. In one year I shipped more new projects than in every earlier year combined: knowledge graphs, honest evaluations, tireless helpers, and an entire fleet of teaching sites. All of it built and run by one person, with a rack of GPUs at home. What every engineer dreamed of - one person building like a team - finally exists. It rewards exactly the people who spent years preparing for it.

  14. Research

    Now

    The room where the voices answer

    The strangest thing I have ever built: I turned the oldest library I know into a knowledge graph, every piece carrying its source, every author joined to their teachers. Then I taught a machine to answer the honest way - name where you read it, or say nothing. Eight voices now answer from that graph, out loud, from a machine in my house, and anyone can talk to them. It is my grounding testbed: the hardest citation problem I could find, running in public.

  15. The rule, measured

    Does the rule hold?

    It holds. Every answer must name a real source you can go and look up, verified word for word against the actual text - and if no source exists, the system says nothing. Only a model that clears every check ships.

    That discipline - cite or stay silent, measured, in public - is what I want to bring to a team working on AI you have to be able to trust. The research behind it is at jew.tech; the productized version is Digital Twin Pro.

Built and running

The sites

49 live sites. Every one of them started as something I wanted to understand well enough to explain out loud. Half my career happened in classrooms nobody called classrooms - teaching calculus, mentoring interns, building learning platforms. These sites are that same instinct with better tools, and every one of them is open to you. Here are six to start with; the rest, with what each one is for, live at jew.tech.

Connect

One conversation I always take

Are you building AI that has to be trusted before it is believed? Do you need someone who ships, measures, and publishes the receipts? Do you want an engineer who has run this exact discipline in production for years, not proposed it in a slide?

I am looking for my next role: AI engineering, grounded systems, the alignment problem in production. That is the conversation I always take. Write to me and I will answer - with sources!

Want the full picture first? The research is at jew.tech, the receipts at jew.tech/evidence, and the experiments I want to run next at jew.tech/agenda.