Forward Deployed Engineer, Data De-Identification

This is a fully remote position, open to applicants in United States.

📋 Description

• Take charge of delivering client de-identification projects from initial raw data to final client acceptance.

• Process corpora through the de-identification pipeline and verify the accuracy of the output.

• Identify, clarify, and resolve issues within the pipeline.

• Rerun pipelines and collaborate with the annotation team to ensure quality validation.

• Develop and modify ingestion and transformation processes for various data sources, including mailboxes, chat archives, documents, ticketing systems, and databases.

• Write code to be integrated into the existing platform, including specific handling for clients.

• Conduct reconciliation checks from data ingestion to delivery.

• Contribute to technical client reports that detail sensitive data management, file handling, and associated limitations.

• Guide clients through the project process and the resulting data.

• Transform one-off fixes into reusable documentation, runbooks, and product enhancements.

• Familiarize yourself with the platform and delivery process, then independently manage projects from scoping to delivery.

⛳️ Requirements

• A bachelor's degree in a technical discipline (such as computer science, engineering, mathematics, statistics, physics, or a related field).

• Several years of practical experience in data engineering, backend engineering, or a solutions/forward-deployed role where you delivered production systems for clients.

• Proficiency in Python and SQL.

• Comfortable with reading and debugging code written by others.

• Hands-on experience with at least one major cloud provider (AWS, GCP, or Azure), covering storage, compute, IAM, networking, and security measures.

• Experience in processing large-scale unstructured and semi-structured data.

• Proven track record of delivering complex projects within tight deadlines, typically within days or weeks.

• Ability to communicate clearly with both technical and executive stakeholders.

• Self-motivated with the capacity to make sound decisions independently.

• Daily engagement with AI coding tools, agents, and LLMs.

• Experience with model inference or machine learning pipelines.

• Exposure to handling unstructured or semi-structured data.

• Interest in collaborating closely with leading AI labs and the training-data ecosystem.

• Previous experience at an early-stage company or with a new product line.

🏝️ Benefits

• Equity.

• Bonus.

• Competitive compensation.

• Collaborate with technical founders.

• Gain exposure to advanced AI labs and the training-data marketplace.