Research Engineer, Privacy and Anonymization

JOB SUMMARY

Kadir.ai is recruiting a Research Engineer, Privacy and Anonymization on behalf of a rapidly growing artificial intelligence company building infrastructure for reinforcement-learning training data and evaluations.

The successful candidate will design, benchmark, and deploy systems that identify and protect sensitive information before data enters processing, training, evaluation, and synthetic-data pipelines. This role requires strong Python and production data or machine-learning experience, careful experimental judgment, and close attention to privacy leakage, edge cases, and adversarial failure modes.

• This is a full-time position.
• San Francisco or Singapore is preferred.
• Remote candidates are welcome if they can maintain approximately 70–80% working-hour overlap with either the San Francisco or Singapore time zone.
• Relocation and visa support are available for strong candidates.
• Applications are reviewed on a rolling basis.
• The selection process includes an initial screening, two technical interviews, and a two-to-three-day structured work trial.

KEY RESPONSIBILITIES

• Detect personally identifiable information, quasi-identifiers, credentials, secrets, and other sensitive content
• Select appropriate privacy transformations based on data type, risk level, and downstream use
• Develop and compare rules-based, statistical, classifier-based, and LLM-assisted detection methods
• Build production anonymization pipelines for data processing, model training, evaluation, and synthetic-data workflows
• Implement redaction, masking, tokenization, pseudonymization, anonymization, and related safeguards
• Measure privacy risk and retained data utility
• Develop recall-weighted metrics and leakage-detection tests
• Conduct adversarial re-identification testing
• Evaluate tradeoffs involving recall, precision, latency, cost, and downstream usefulness
• Handle new data sources, schema drift, unusual formats, and unexpected sensitive fields
• Identify privacy leakage paths and edge cases across complex data pipelines
• Translate customer and internal privacy requirements into practical technical policies
• Monitor anonymization performance and investigate production failures
• Document methodologies, risks, decisions, limitations, and validation results
• Collaborate with research, engineering, platform, security, and customer-facing teams

REQUIRED SKILLS

• Strong Python programming ability
• Experience building production data or machine-learning systems
• Experience with information extraction, named-entity recognition, or classification
• Understanding of personally identifiable information and quasi-identifiers
• Knowledge of redaction, masking, pseudonymization, anonymization, and synthetic data
• Experience designing and evaluating detection systems
• Strong experimental judgment across recall, precision, latency, cost, and utility
• Experience developing metrics, tests, and validation procedures
• Strong attention to leakage paths, edge cases, and adversarial failure modes
• Experience processing structured, semi-structured, and unstructured data
• Strong debugging and analytical problem-solving abilities
• Clear written and verbal communication
• Ability to work independently in an ambiguous, fast-moving environment
• Ability to maintain approximately 70–80% working-hour overlap with San Francisco or Singapore

PREFERRED QUALIFICATIONS

• Experience with privacy-enhancing technologies
• Experience processing sensitive healthcare, financial, security, or identity data
• Knowledge of high-throughput inference systems
• Experience with LLM-based information extraction or classification
• Familiarity with data-loss prevention tools and privacy-preserving pipelines
• Knowledge of privacy-risk measurement and re-identification testing
• Experience with synthetic-data systems
• Familiarity with regulatory privacy requirements
• Experience managing schema drift and changing production-data formats
• Experience deploying containerized data or ML pipelines
• Early-stage startup experience
• Published research, technical writing, open-source work, or independent privacy projects

EXPERIENCE REQUIRED

• Demonstrated experience in one or more of the following areas:
• Privacy engineering
• Machine-learning engineering
• Research engineering
• Data engineering
• Natural-language processing
• Information extraction
• Named-entity recognition
• Applied artificial intelligence
• Experience building or evaluating systems that detect sensitive information
• Experience deploying reliable data-processing or machine-learning pipelines
• Technical judgment, research ability, and production ownership are more important than a fixed number of years of experience

EDUCATION REQUIRED

• Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, Mathematics, Cybersecurity, or a related field is preferred
• A master’s degree, PhD, or relevant research experience is beneficial but not required
• Equivalent professional experience, independent research, or a strong technical portfolio will be considered

TOP SEVEN SKILLS

• Python
• Privacy Engineering
• Data Anonymization
• Natural Language Processing
• Named-Entity Recognition
• Machine-Learning Systems
• Data-Pipeline Development

HIRING PROCESS

• Application and résumé review
• Initial Kadir.ai screening
• Two client technical interviews
• Two-to-three-day structured work trial
• Final decision and offer

EQUAL OPPORTUNITY

Kadir.ai is an equal opportunity recruiting partner. Qualified applicants will be considered without regard to race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other status protected by applicable law.

Pay: $100,000.00 - $200,000.00 per year

Work Location: Remote