layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

Research Engineer, Privacy and Anonymization at HUD | LAYIQ

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.

Research Engineer, Privacy and Anonymization

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