Research Engineer, Privacy and Anonymization

HUD; United States; À distance; USD 140 000 - 210 000; Plein temps

Avantages offerts par ce poste

Competitive compensation

Top-tier medical/dental/vision

Lunch and dinner in office (onsite)

Holiday break + PTO

401k

Commuter benefits

Unlimited access to tokens for AI APIs

Résumé du poste

HUD is seeking a Research Engineer to build privacy and anonymization systems for sensitive real-world data used in AI training. You will develop methods to detect and remove PII, secrets, and other sensitive information before data enters processing and synthetic data pipelines.

You will own end-to-end privacy protection while preserving data structure and utility, working with engineering, research, operations, and customers. This is a full-time, remote-friendly role with visa-support options.

Qualifications

• Strong proficiency in Python and experience building reliable production data or ML systems.
• Experience with information extraction, named-entity recognition, classification, or related methods for detecting rare or sensitive content.
• Strong experimental instincts and the ability to compare approaches across recall, precision, latency, cost, and downstream data utility.
• An understanding of redaction, masking, pseudonymization, anonymization, and synthetic data—and when each is appropriate.
• High attention to detail and the ability to reason about subtle leakage paths, edge cases, and adversarial failure modes.
• Built data processing pipelines end-to-end without a fully prescribed roadmap.

Responsabilités

• Build systems to detect PII, quasi-identifiers, credentials, and other sensitive information and design transformations based on the data type and downstream use case.
• Develop and benchmark detection approaches that combine rules, statistical models, classifiers, and LLM-based methods.
• Build production pipelines that anonymize raw data before it enters downstream processing, training, evaluation, or synthetic data generation workflows.
• Create evaluation frameworks that measure privacy risk and retained data utility, including recall-weighted metrics, leakage tests, and adversarial re-identification attempts.
• Design systems that remain robust to new data sources, schema drift, unusual formats, and sensitive information embedded in unexpected fields.
• Work with engineering, research, operations, and customers to translate privacy requirements into practical technical policies and safeguards.

Connaissances

Python

Information extraction

Experimental design

Privacy concepts

Data pipelines

Attention to detail

Cross-team collaboration

Outils

Production ML systems

Description du poste

About HUD

HUD is building infrastructure to create RL training data and evals for frontier AI agents, as well as a marketplace to sell these to frontier labs through the HUD marketplace. Our platform is used by frontier labs, Fortune 500 companies, and startups. We’ve raised $16M from top VCs and were YC W25.

About the role

We’re looking for a Research Engineer to build the privacy and anonymization systems that make sensitive, real-world data safe and useful for AI training. You’ll develop methods to detect and remove PII, secrets, and other sensitive information from raw data before it enters our processing and synthetic data pipelines. You’ll own the full pipeline for protecting privacy without destroying the structure and signal that make data valuable for training agents.

Responsibilities

• Build systems to detect PII, quasi-identifiers, credentials, and other sensitive information and design transformations based on the data type and downstream use case

• Develop and benchmark detection approaches that combine rules, statistical models, classifiers, and LLM-based methods

• Build production pipelines that anonymize raw data before it enters downstream processing, training, evaluation, or synthetic data generation workflows

• Create evaluation frameworks that measure privacy risk and retained data utility, including recall-weighted metrics, leakage tests, and adversarial re-identification attempts

• Design systems that remain robust to new data sources, schema drift, unusual formats, and sensitive information embedded in unexpected fields

• Work with engineering, research, operations, and customers to translate privacy requirements into practical technical policies and safeguards

Experience

You may be a good fit if you have:

• Strong proficiency in Python and experience building reliable production data or ML systems

• Experience with information extraction, named-entity recognition, classification, or related methods for detecting rare or sensitive content

• Strong experimental instincts and the ability to compare approaches across recall, precision, latency, cost, and downstream data utility

• An understanding of the difference between redaction, masking, pseudonymization, anonymization, and synthetic data—and when each is appropriate

• High attention to detail and the ability to reason about subtle leakage paths, edge cases, and adversarial failure modes

• Built data processing pipelines end-to-end without a fully prescribed roadmap

Strong candidates may also have:

• Hands-on experience with privacy-enhancing technologies such as differential privacy, k-anonymity, secure aggregation, format-preserving encryption, etc.

• Worked with sensitive data in areas such as healthcare, finance, or security

• Built low-latency or high-throughput ML inference and data-processing systems

• Worked in unstructured problem spaces and take ownership from early research through production deployment

• Early-stage startup experience and strong communication skills for collaboration across teams and time zones

We prioritize technical aptitude and learning potential over years of experience. Motivated candidates are encouraged to apply even if they don’t meet all criteria.

Team & company details

• Team Size : ~25 people currently, mostly full-time in-person, but some remote.

• Our team: Our team includes 4 International Olympiad medalists (IOI, ILO, IPhO), serial AI startup founders, and researchers with publications at ICLR, NeurIPS, etc.

• Company stage: We have 8 figures in funding and are scaling profitably and quickly to meet very strong demand.

Logistics

• Employment : Full-time.

• Location : We have offices in San Francisco or Singapore but are open to remote candidates who can work hours that 70-80% overlap with either San Francisco or Singapore time zones.

• Visa Sponsorship : We provide support for relocation and visas for strong full-time candidates to the US or Singapore.

• Timeline : Applications are rolling. The process is 2 technical interviews and a 2-3 day work trial.

What we offer

• Competitive compensation

• 100% covered top-of-the-line medical, dental, and vision from Blue Shield of CA (US employees)

• Lunch and dinner when you’re in the office (in-office employees)

• Company-wide holiday break (Christmas Eve to New Year’s Day) on top of PTO and paid holidays

• Other perks including an Equinox membership, 401k, and commuter benefits (US employees)

• Unlimited* access to tokens for ChatGPT, Claude Code, Cursor, etc. *By unlimited, we mean no one on our token usage leaderboard has ever hit a limit. So we have no idea what the limit is.

Due to high volume, we may not actively respond to every application, but feel free to contact us at [e-mail] or elsewhere if we missed your application!