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Tonic.ai is a Series B AI company that de-identifies sensitive data for enterprise clients and AI labs. The company has been operating since 2018 and counts eBay, UnitedHealth Group, Qualcomm, Commonwealth Bank, and Mercor among its customers. The newest business line focuses on de-identifying company operating data—email, chat, tickets, documents, code, CRM and HR records—to enable safe training of frontier AI models.
As a Forward Deployed Engineer, you will own the end-to-end delivery of client de-identification projects. You'll take raw, messy data exports from new clients, run them through Tonic's de-identification pipeline, validate output for accuracy (catching missed entities, wrong replacements, identity merges/splits, and file rebuild failures), and ship datasets that both the client and end-buyer AI labs will sign off on.
Key responsibilities include:
- Running corpora through the de-identification pipeline and auditing output quality
- Diagnosing pipeline failures and either fixing them directly or explaining them clearly to platform engineers
- Building and adjusting ingestion and transformation steps for various data sources (mailboxes, chat archives, document dumps, ticketing systems, databases)
- Writing production code within the existing platform, including custom handling for client-specific edge cases
- Running reconciliation checks to verify data integrity from ingest to delivery
- Contributing to technical sections of client reports (what was treated as sensitive, handling methodology, known limitations)
- Presenting results to clients alongside founders initially, then independently as you grow
- Converting one-off fixes into reusable documentation, runbooks, and product improvements
You'll be one of the first engineers dedicated to this business line, working directly with technical founders, data scientists, and platform engineers, and gaining exposure to frontier AI labs that are the end buyers of this data. Demand is outpacing delivery capacity, and clients expect datasets in days to weeks, not quarters.
REQUIREMENTS:
- Bachelor's degree in a technical field (computer science, engineering, math, statistics, physics, or similar)
- Several years of hands-on experience in data engineering, backend engineering, or solutions/forward-deployed roles where you shipped production systems for customers
- Strong Python and SQL; comfortable reading and debugging others' code
- Real experience with at least one major cloud platform (AWS, GCP, or Azure): storage, compute, IAM, networking, and security controls
- Experience processing large volumes of unstructured and semi-structured data (PST files, mbox, Slack exports, spreadsheets, etc.)
- Track record of delivering complex projects on short timelines (days and weeks)
- Clear communication with diverse audiences (engineers and senior executives)
- Self-direction and ability to make sound decisions independently
- AI-native habits: daily use of AI coding tools, agents, and LLMs with informed opinions on their effectiveness
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
