Principal ML Engineer, Anonymization & Privacy Platform

Workday, Inc.; Dublin; Hybrid; EUR 116,000 - 174,000; Full time

Benefits offered by this job

Flexible work arrangements

Stock grants

Job summary

Workday, Inc. is seeking a Principal Machine Learning Engineer focused on anonymization in Dublin. You will lead the new capability for anonymized datasets, collaborating with external researchers, setting technical direction, and delivering a scalable platform consumed by other teams.

You will drive privacy-preserving ML, differential privacy, and synthetic data approaches while partnering with Product Legal, compliance, and executives to align strategy with de-identification standards.

Qualifications

• 10+ years of hands-on experience in Machine Learning Engineering, Data Science, or applied research, including leading technical initiatives from research through enterprise production deployment.
• Demonstrable depth in privacy-preserving ML techniques (differential privacy, group anonymization, or synthetic data generation).
• Expert-level Python and ML frameworks (PyTorch or TensorFlow).
• Experience architecting large-scale data processing pipelines with Spark or similar.

Responsibilities

• Own the technical vision, architecture, and roadmap for anonymization capabilities.
• Lead applied privacy research and benchmarking for privacy-preserving ML.
• Build and evaluate anonymization models, balancing privacy and utility.
• Lead a group of engineers, set architectural standards, and mentor staff.

Skills

ML engineering

Python

PyTorch

TensorFlow

Privacy-preserving ML

Differential privacy

Group anonymization

Synthetic data generation

Spark

Technical leadership

Education

Bachelor’s degree in CS/Physics/Math

Tools

PyTorch

TensorFlow

Spark

Job description

Workday, Inc. is seeking a Principal Machine Learning Engineer focused on anonymization in Dublin. You will lead the new capability for anonymized datasets, collaborating with external researchers, setting technical direction, and delivering a scalable platform consumed by other teams.

You will drive privacy-preserving ML, differential privacy, and synthetic data approaches while partnering with Product Legal, compliance, and executives to align strategy with de-identification standards.