Forward Deployed Engineer, Data De-Identification
Tonic AI · Remote · Remote ·
COMPENSATION
$120k – $180k
Listed on the employer's job board (Ashby)
- Fully remote
- Python + SQL
- Data engineering focus
- AI-native workflow
- Early-stage team
You will own the end-to-end delivery of client de-identification projects, transforming raw corporate data into safe, usable training sets for AI labs. Working closely with founders and data scientists, you will diagnose pipeline issues, validate output quality, and ensure consistent synthetic identity replacement across various file formats.
What you'll do
- Manage client projects from raw data intake to final accepted dataset delivery
- Execute de-identification pipelines and verify output for accuracy and consistency
- Diagnose and fix pipeline errors or document issues for engineering resolution
- Build ingestion steps for diverse data sources like emails, chats, and databases
- Write production code within platform guardrails for client-specific needs
- Perform reconciliation checks to ensure file counts and content integrity
- Draft technical report sections detailing sensitive data handling and limitations
- Present results to clients and convert one-off fixes into reusable documentation
What you'll need
- Bachelor’s degree in computer science, engineering, math, or related field
- Several years in data engineering, backend, or forward-deployed engineering roles
- Strong proficiency in Python, other backend languages, and SQL
- Experience building repeatable pipelines for large volumes of mixed data types
- Comfortable reading, debugging, and modifying existing codebases
- Proficient with AI tools and agents, verifying outputs rather than trusting blindly
- Self-directed ability to make sound technical decisions independently
- Clear communication skills for both technical engineers and senior executives
- Proven track record of delivering complex projects on short timelines
Nice to have
- Experience with model inference, ML pipelines, or PyTorch
- Background in data masking, de-identification, or synthetic data tooling
- Interest in frontier AI labs and the training-data ecosystem
- Previous experience at an early-stage company or new product line
Stack
- Python
- SQL
- PyTorch
What you get
- Competitive compensation package
- Equity ownership in the company
- Direct influence on product development via founder collaboration
- Exposure to frontier AI labs and training-data market dynamics