Forward Deployed Engineer, Data De-Identification

Role overview Own technical delivery for projects that prepare sensitive company data for AI use by replacing personal and organizational identities with consistent synthetic ones. You’ll take varied, imperfect data collections from intake through processing, quality review, and client acceptance, while turning project-specific fixes into reusable tools and practices.

Responsibilities - Scope incoming data and run it through de-identification workflows, checking for missed sensitive information, incorrect substitutions, identity mismatches, and files that fail to rebuild. - Investigate processing issues, implement targeted or general fixes where possible, and work with engineering or annotation colleagues when further support is needed. - Build and adapt ingestion and transformation steps for exports such as email, chat, documents, ticketing records, and databases. - Reconcile file counts and content from intake through delivery, then document processing choices, retained data, and known limitations. - Explain results and technical trade-offs to client engineers and business stakeholders, with increasing ownership of client discussions. - Create runbooks, documentation, and product improvements based on lessons from each engagement.

Requirements - Bachelor’s degree in a technical discipline such as computer science, engineering, mathematics, statistics, or physics. - Several years delivering production systems in data engineering, backend engineering, or customer-facing solutions engineering. - Strong Python and SQL skills, with the ability to read and debug code written by others. - Practical experience with a major cloud platform, including storage, compute, access controls, networking, and security. - Experience processing large volumes of unstructured or semi-structured data, or the ability to quickly understand unfamiliar formats. - Evidence of delivering complex work on short timelines, communicating clearly with both technical and executive audiences, and working independently. - Regular use of AI coding tools or language models, with sound judgment about their strengths and limits.

Nice to have Experience with model inference or machine-learning pipelines, early-stage teams or new product lines, and interest in AI training data.

Benefits and work setup The role offers competitive compensation and equity. Early work includes learning the platform and contributing to a live engagement; the aim is to take projects from scoping through delivery independently within the first quarter.