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Tonic.ai has been de-identifying sensitive data since 2018, and hundreds of enterprises use our products, among them eBay, UnitedHealth Group, Qualcomm, Commonwealth Bank, and Mercor. Our newest line of business takes that work somewhere it hasn't gone before: company operating data. Email, chat, tickets, documents, code, CRM and HR records are some of the most valuable training data left for AI labs, and almost none of it can change hands until the people in it are protected.
We sit between the company that owns the data and the lab that wants it, as the independent third party. We take in the corpus, find the people and organizations referenced across all of those systems, give each real identity one consistent synthetic replacement, rebuild every file in its original format, and deliver the result with a written report on how it was done. The data still reads like a company doing its work, which is what makes it worth training on.
Demand is running ahead of our capacity to deliver. Labs are buying, data owners are selling, and the buyers want datasets in days to weeks, not quarters. The technology can meet that timeline. What turns it into a delivered dataset is an engineer who can take a messy, half-documented corpus from a new client, get it through the pipeline, catch what breaks, and ship something both sides will sign off on.
That's this job. You’ll be one of the first engineers dedicated to this business, working directly with our technical founders, data science team and platform engineers, and getting close to the frontier AI labs that are the end buyers of this data.
Own the delivery of client de-identification projects, from the first look at the raw data to a dataset the client accepts.
Run each corpus through our de-identification pipeline and check the output for accuracy: missed entities, wrong replacements, identities merged or split incorrectly, files that won’t rebuild.
Diagnose problems as they appear. Where you can, fix them for the whole solution or for the one case. Where you can’t, understand the issue well enough to explain it clearly to the engineer who can.
Rerun the pipeline, confirm the fix worked, then move on to the next step, which often includes working with our annotation team to validate quality.
Build and adjust the ingestion and transformation steps that turn raw exports (mailboxes, chat archives, document dumps, ticketing systems, databases) into processed, ready-to-use datasets.
Write code that gets merged, within the guardrails of our existing platform, including custom handling for client-specific one-offs.
Run reconciliation checks so file counts and content tie from ingest to delivery.
Contribute to the technical sections of the client report: what was treated as sensitive, what was kept and why, how each file type was handled, and known limitations.
Walk clients through what was done and the resulting data, alongside Tonic’s founders at first and on your own as you grow.
Turn every one-off fix into something reusable: documentation, runbooks, and improvements to the product.
A bachelor’s degree in a technical field (computer science, engineering, math, statistics, physics or similar).
Several years of hands-on work in data engineering, backend engineering, or a solutions/forward-deployed role where you shipped production systems for customers.
Strong Python and SQL. Comfortable reading and debugging other people's code.
Real experience with at least one major cloud (AWS, GCP or Azure): storage, compute, IAM, networking, and the security controls clients will ask about.
Experience processing large volumes of unstructured and semi-structured data. You know what a PST, an mbox, a Slack export or a 40-tab spreadsheet looks like on the inside, or you can figure it out in an afternoon.
A record of delivering complex projects on short timelines, measured in days and weeks.
Clear communication with very different audiences. You can walk a client's engineer through a pipeline failure in the morning and explain a tradeoff to a senior executive in the afternoon.
Self-direction. You'll often be the only engineer on an engagement, and you'll need to make sound calls without waiting for someone to tell you what to do.
AI-native habits. You use AI coding tools, agents and LLMs in your daily work as a matter of course, and you have opinions about where they help and where they don't.
Experience with model inference or ML pipelines.
Exposure to processing unstructured or semi-structured data (email, chat exports, documents).
Interest in working close to frontier AI labs and the training-data ecosystem.
Experience at an early-stage company or on a new product line.
In your first month, you'll learn the platform and the delivery process and run part of a live engagement alongside the team. By the end of your first quarter, you'll be running engagements on your own, from scoping to delivery, and the runbooks and tooling you've written will have made the second and third ones faster than the first. After that, you'll help decide how this function grows, including who we hire next.
You’ll be early in a fast-growing business solving a problem the AI industry doesn’t yet have a good answer for.
You’ll work alongside technical founders, with a direct line from what you see in the field to what gets built.
You’ll get close to the frontier AI labs and build experience at the center of the training-data market.
The work matters beyond any one deal: getting company data to labs without exposing the people in it is a problem the whole industry has to solve.
Competitive compensation and equity.
