Research Engineer, Privacy and Anonymization

Job not on LinkedIn

🔥 1 hour ago

🌐 United States, Singapore, +4 more countries – Remote

🇺🇸 United States – Remote 🇸🇬 Singapore – Remote 🇨🇴 Colombia – Remote 🇨🇦 Canada – Remote 🇨🇷 Costa Rica – Remote 🇲🇽 Mexico – Remote

🏄 California – Remote

🏄 California – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

📚 Research Engineer

👻 Ghost score 13%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.Why this score:

• Just posted.
• Jobs at this company usually fill in 33 days.
• No salary listed.

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Pragmatike

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2022

💼 Consulting

📣 Marketing

🎯 Recruiter

Consulting • Marketing • Recruitment

Pragmatike is a remote IT recruitment and staffing company that sources, vets, and places international tech talent for businesses. They provide human-curated matching (not solely AI), technical assessments, and fast placements—claiming qualified specialists can be introduced within 48 hours—while handling onboarding, invoicing and international payroll. Pragmatike serves startups and enterprises with roles like developers, data engineers, mobile and game developers, product managers and specialists, operating across 60+ countries with a large vetted talent pool.

📋 Description

• Build systems to detect PII, quasi-identifiers, credentials and other sensitive information • Design data transformations based on data type and downstream use case • Develop and benchmark detection approaches using rules, statistical models, classifiers and LLM-based methods • Build production anonymization pipelines before downstream processing, training, evaluation or synthetic data generation • Create evaluation frameworks measuring privacy risk and retained data utility • Implement recall-weighted metrics, leakage tests and adversarial re-identification attempts • Design systems robust to new data sources, schema drift, unusual formats and sensitive information in unexpected fields • Work with engineering, research, operations and customers to translate privacy requirements into technical policies and safeguards • Own the pipeline in front of downstream processing, training, evaluation and synthetic generation

🎯 Requirements

• Strong proficiency in Python • Experience building reliable production data or machine-learning systems • Experience with information extraction, named-entity recognition, classification, or related methods for detecting rare or sensitive content • Ability to compare approaches across recall, precision, latency, cost and downstream data utility • Understanding of redaction, masking, pseudonymization, anonymization and synthetic data, and when each is appropriate • High attention to detail and ability to reason about subtle leakage paths, edge cases and adversarial failure modes • Experience building end-to-end data-processing pipelines without a fully prescribed roadmap • Clear written and spoken English • Comfort collaborating across teams and time zones • Technical aptitude and learning potential • Candidates may have experience with differential privacy, k-anonymity, secure aggregation or format-preserving encryption • Candidates may have worked with sensitive data in healthcare, finance or security • Candidates may have built low-latency or high-throughput ML inference or data-processing systems • Candidates may have taken work from early research through production • Ability to overlap approximately 70–80% with San Francisco US Pacific or Singapore business hours

🏖️ Benefits

• Equal employment opportunity and non-discrimination commitment • Fair and inclusive hiring process • Personal data processed solely for recruitment purposes with reasonable safeguards • Full-time role • Remote work option for Singapore or US Pacific time zones • On-site work option in San Francisco or Singapore • Opportunity to build privacy systems enabling sensitive data use for AI training • Opportunity to work on privacy, anonymization, data utility, schema drift and adversarial failure modes • Collaboration with engineering, research, operations, customers and international teams

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