ML Engineer - Anonymization & Privacy at Scale

Workday, Inc.; Dublin; Hybrid; EUR 80,000 - 120,000; Full time

Job summary

Workday, Inc. in Ireland is seeking a Machine Learning Engineer to build and tune the models behind ogham, our de-identification engine, and extend anonymization across Workday’s AI platforms.

You will implement privacy techniques such as differential privacy, group anonymization (k-anonymity, l-diversity), and work with platform, infra and legal teams to ensure scalable, compliant ML systems in production.

Qualifications

• 5+ years hands-on experience in Machine Learning Engineering or Data Science.
• Proficient in Python and ML frameworks (PyTorch or TensorFlow).
• Experience building large-scale data pipelines using Spark or equivalent.
• Production ML on AWS or similar cloud platform.
• Bachelor's degree in CS, Physics, Mathematics or related field.

Responsibilities

• Anonymization & Privacy Engineering: apply privacy techniques across real enterprise use cases and translate results into governance actions.
• Model Optimization & Fine-Tuning: build and improve de-identification and anonymization models (NER, pattern matching) balancing accuracy and latency.
• Pipeline Ownership & Operations: manage data exploration, feature/prompt engineering, and model design across Spark/EMR/SageMaker pipelines in production.
• Cross‑Functional Collaboration: work with platform, infra, and legal teams to implement governance frameworks.
• Technical Communication: document results and decisions for technical, legal, and customer audiences.

Skills

Python

ML frameworks (PyTorch / TensorFlow)

Spark / distributed data

AWS / Cloud

ML engineering

Education

Bachelor's degree in Computer Science, Physics, Mathematics

Tools

Spark

EMR

SageMaker

Hugging Face

Job description

Workday, Inc. in Ireland is seeking a Machine Learning Engineer to build and tune the models behind ogham, our de-identification engine, and extend anonymization across Workday’s AI platforms.

You will implement privacy techniques such as differential privacy, group anonymization (k-anonymity, l-diversity), and work with platform, infra and legal teams to ensure scalable, compliant ML systems in production.