Principal Machine Learning Engineer at Workday, Inc.

Compensation · Listed in posting

€116,000 – €174,000/yr

Setup: Hybrid
Location: Dublin
Type: Full-time
Level: staff+
Posted: Sep 17, 2026

Description

Workday is hiring a Principal Machine Learning Engineer for anonymization in Dublin to lead the technical vision, architecture, roadmap, applied research, and production platform for anonymized datasets used in research, benchmarking, synthetic data generation, and agent evaluation. The role will calibrate privacy parameters against utility, evaluate adversarial attacks, lead a group of engineers, set standards, and collaborate with legal, compliance, product, executive, and external research partners. Candidates need 10+ years of machine learning engineering or applied research experience, expertise in privacy-preserving techniques, Python and modern ML frameworks, large-scale distributed data pipelines, and technical leadership experience.

Required Skills

Python
PyTorch
TensorFlow
Spark
Differential privacy
Group anonymization
Synthetic data
Data governance
Technical leadership

Additional Information

Minimum Education: Bachelor’s degree in Computer Science, Physics, Mathematics, or a related quantitative field, or equivalent practical experience
Required Previous Experiences: 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 machine learning, including differential privacy, group anonymization, or synthetic data generation
Experience architecting and operating large-scale data processing pipelines using Spark or equivalent distributed frameworks
Experience leading engineers through ambiguous technical problems, setting cross-team architecture standards, and mentoring mid-level and senior engineers
Preferred Qualifications: Experience with classification, NER, transformer architectures, LLM fine-tuning, and GPU inference optimization
Master’s or Ph.D. in Computer Science, Machine Learning, Statistics, or a related quantitative discipline
Experience deploying, scaling, and maintaining machine learning systems in production on AWS or an equivalent cloud platform
Experience with agent execution, orchestration, or LLM evaluation frameworks involving sensitive data at scale
Understanding of responsible AI, bias and fairness evaluation, and privacy regulatory frameworks such as GDPR
Experience directing external research partners, consultancies, or academic collaborators
Publications, patents, open-source contributions, or conference work in privacy-preserving machine learning