Forward Deployed Engineer, Data De-Identification

Tonic.ai has been de-identifying sensitive data since 2018. Its newest line of business is company operating data (email, chat, tickets, documents, code, CRM and HR records) prepared as training data for AI labs. The role is one of the first engineering positions dedicated to this business, working with technical founders, the data science team, and platform engineers, and close to frontier AI labs that are the end buyers of this data. The engineer owns client de-identification projects end to end, from raw data review to client-accepted delivery.

Responsibilities

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 the de-identification pipeline and check the output for accuracy; Diagnose problems as they appear and fix them for the whole solution or for the one case; Rerun the pipeline, confirm fixes, and work with the annotation team to validate quality; Build and adjust ingestion and transformation steps that turn raw exports into processed, ready-to-use datasets; Write code that gets merged, 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; Walk clients through what was done and the resulting data; Turn every one-off fix into reusable documentation, runbooks, and product improvements

Requirements

A bachelor's degree in a technical field (computer science, engineering, math, statistics, physics, bioinformatics, 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 experience in Python and other backend programming languages, plus SQL; Comfortable reading and debugging other people's code; Experience building repeatable pipelines that process large volumes of structured, semi-structured, and unstructured data; AI-native workflow, with AI tools and agents central to how you work; Self-direction, with the ability to make sound calls without waiting for instructions; Clear communication with very different audiences; A record of delivering complex projects on short timelines, measured in days and weeks

Nice to have

Experience with model inference, ML pipelines, or PyTorch; Experience building or maintaining data masking, de-identification, ETL or synthetic data tooling; 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

Benefits

Competitive compensation; Equity