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# Forward Deployed Engineer, Data De-Identification

- **Company:** TONIC
- **Location:** United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Spreadsheets, Databases, Information Engineering, Software Debugging, Identity and Access Management, Python (Programming Language), SQL Databases, Large Language Models, AI Coding Agents, Semi-structured Data, Information Technology, Machine Learning Operations
- **Published:** October 13, 2026
- **Apply:** https://startup.jobs/forward-deployed-engineer-data-de-identification-tonic-ai-10422125

## About the Role

* 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.

Nice to haves

 * 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.

## Description

* 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.