About HUD

HUD's mission is to build reliable, fair and open infrastructure for AI data. We want data to be valuable for the people who create it and trustworthy for the labs that train on it. Our team is a quickly growing group of researchers, engineers and operators building the economy that shapes what AI will become. Backed by $16M from top VCs and YC (W25), our marketplace and platform are used by startups, Fortune 500 companies and frontier labs.

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 (US and Singapore employees)
Lunch and dinner for 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) and a health/wellness stipend (Singapore 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.
Two top-floor offices in San Francisco’s Union Square and Singapore’s Raffles Place
Annual travel budget to visit either office

Compensation

Actual offers are adjusted for experience and location, but our base salary bands are

San Francisco (and other major US cities): $135,000 - $230,000
Singapore: $100,000 - $175,000
Rest of world: $100,000 - $175,000

Due to high volume, we may not actively respond to every application, but feel free to contact us at recruiting@hud.so or elsewhere if we missed your application!

Research Engineer, Privacy and Anonymization