Forward Deployed Engineer, Data De-Identification

🔥 0 minutes ago

🇺🇸 United States – Remote

⏱ Part Time

⚪️ Entry-level

🚰 Data Engineer

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Tonic.ai

WebsiteLinkedInAll Job Openings

51 - 200 employees

🏥 Healthcare

📦 Logistics

💼 Consulting

💰 $35M Series B on 2021-09

Healthcare • Logistics • Consulting

Tonic. ai is a company that provides software solutions for data security and test data management. They specialize in generating realistic synthetic data to safely test and develop applications without using sensitive real data. Their products focus on data de-identification, data masking, and synthesis, making it easier for engineers to create development environments that mirror production conditions. Tonic. ai also integrates with various relational and NoSQL databases, supporting AI development by transforming unstructured data into safe, usable formats. They cater to industries like healthcare and financial services with compliance needs such as HIPAA and GDPR.

📋 Description

• Own delivery of client de-identification projects from raw data review through client acceptance • Run corpora through the de-identification pipeline and check outputs for missed entities, incorrect replacements, merged or split identities, and file rebuild failures • Diagnose issues, fix them when possible, and clearly explain unresolved problems to responsible engineers • Rerun pipelines and confirm fixes, often working with the annotation team to validate quality • Build and adjust ingestion and transformation steps for mailboxes, chat archives, document dumps, ticketing systems, and databases • Write code within the existing platform, including client-specific custom handling • Run reconciliation checks to ensure file counts and content match from ingestion through delivery • Contribute to technical sections of client reports covering sensitive data treatment, retained data, file-type handling, and known limitations • Walk clients through the work and resulting data alongside founders initially, then independently as experience grows • Convert one-off fixes into reusable documentation, runbooks, and product improvements • Learn the platform and delivery process, run part of a live engagement, and progress to independently managing engagements from scoping to delivery

🎯 Requirements

• Bachelor's degree in a technical field (computer science, engineering, math, statistics, physics, bioinformatics, or similar), or currently enrolled in one • Hands-on work in data engineering, backend engineering, or a solutions/forward-deployed role shipping production systems for customers • Internships, co-ops, research, and substantial projects count • Strong experience in Python and other backend programming languages, plus SQL • Comfortable reading and debugging other people's code • Experience building repeatable pipelines processing large volumes of structured, semi-structured, and unstructured data • Understanding of data flow through systems and ability to identify incorrect output • Experience using AI tools and agents as a central part of the workflow, with appropriate oversight and validation • Ability to work independently and make sound decisions without waiting for direction • Clear communication with technical and executive audiences • Record of delivering complex projects on short timelines, measured in days and weeks • Availability for roughly 20 hours a week, with flexibility to keep client engagements on schedule • Experience with model inference, ML pipelines, or PyTorch is a nice-to-have • Experience with data masking, de-identification, ETL, or synthetic data tooling is a nice-to-have • Interest in working close to frontier AI labs and the training-data ecosystem is a nice-to-have • Experience at an early-stage company or on a new product line is a nice-to-have

🏖️ Benefits

• Competitive compensation • Opportunity to work alongside technical founders • Direct impact on product development • Exposure to frontier AI labs and the training-data market • Experience solving industry-wide data privacy challenges • Remote work arrangement • Part-time co-op schedule of roughly 20 hours per week

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