Machine Learning Engineer at Workday

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

Job summary

Workday is seeking a seasoned Machine Learning Engineer to build and optimize the models and data pipelines behind ogham, our de-identification engine. You will help grow anonymization at scale, collaborating with cross-team partners across Workday’s AI Platform in Dublin.

The role focuses on implementing differential privacy, k-anonymity, l-diversity, and t-closeness, while ensuring privacy-utility balance and policy alignment with legal/compliance teams.

Qualifications

• 5+ years of hands-on experience in Machine Learning Engineering or Data Science (or equivalent research experience via a Ph.D.).
• Proficiency in Python and modern ML frameworks such as PyTorch or TensorFlow.
• Data pipelines: building and operating large-scale data processing pipelines using Spark or equivalent distributed frameworks.
• Cloud & Production: deploying, scaling, and maintaining ML systems in production on AWS (or equivalent cloud platform).
• Education: Bachelor’s degree in Computer Science, Physics, Mathematics, or a related quantitative field (or equivalent practical experience).

Responsibilities

• Anonymization & Privacy Engineering: Apply and evaluate privacy techniques across real enterprise use cases, measuring the privacy-utility trade-off and turning findings into actionable recommendations for data governance.

Skills

Python

PyTorch

TensorFlow

Spark

AWS

Machine Learning

Education

Bachelor's degree in Computer Science, Physics, Mathematics, or related field

Tools

EMR

SageMaker

Spark

Job description

This Full time on site position offers great opportunities for career growth. Your work days are brighter here. We’re obsessed with making hard work pay off, for our people, our customers, and the world around us. As a Fortune 500 company and a leading AI platform for managing people, money, and agents, we’re shaping the future of work so teams can reach their potential and focus on what matters most. The minute you join, you’ll feel it. Not just in the products we build, but in how we show up for each other. Our culture is rooted in integrity, empathy, and shared enthusiasm. We’re in this together, tackling big challenges with bold ideas and genuine care. We look for curious minds and courageous collaborators who bring sun-drenched optimism and drive. Whether you're building smarter solutions, supporting customers, or creating a space where everyone belongs, you’ll do meaningful work with Workmates who’ve got your back. In return, we’ll give you the trust to take risks, the tools to grow, the skills to develop the support of a company invested in you for the long haul. So, if you want to inspire a brighter work day for everyone, including yourself, you’ve found a match in Workday, and we hope to be a match for you too.

About the Team

Danu is part of Workday’s AI Centre of Excellence in Dublin, operating within the AI Platform organization to support a customer base of 60 million users. Danu’s charge is AI privacy — researching and translating sophisticated challenges in human-AI partnership, ML model performance, Explainable AI (XAI), and Responsible AI into production capabilities, with a specific focus on anonymization. Our de-identification engine, ogham — named for Ireland's earliest alphabet — already detects PII at industry-leading recall and efficiency at enterprise scale. We are now extending our privacy engineering capabilities to build the next chapter: a dedicated anonymization capability that will allow Workday and its customers to set new industry-leading privacy standards.

About the Role

As a Machine Learning Engineer on Danu, you will build and tune the machine learning models and data pipelines behind ogham, our de-identification engine, detecting and redacting sensitive data across Workday’s AI systems. You will help grow de-identification into a broader anonymization capability, working at scale in close partnership with agent-platform teams across Workday.

Your First Six Months

You will focus on building out our anonymization capability — implementing and evaluating differential privacy and group anonymization techniques such as k-anonymity, l-diversity, and t-closeness against real research use cases, and establishing the evaluation approach that the broader platform capability will be built on.

Key Responsibilities

• Anonymization & Privacy Engineering: Apply and evaluate privacy techniques across real enterprise use cases, measuring the privacy-utility trade-off and turning findings into actionable recommendations for data governance.
• Model Optimization & Fine-Tuning: Build, fine-tune, and continuously improve de-identification and anonymization models — including Named-Entity Recognition (NER) and pattern-matching layers — balancing accuracy, latency, and compute efficiency.
• Pipeline Ownership & Operations: Own the full lifecycle of data exploration, transformation, feature and prompt engineering, and model design across high-throughput Spark, EMR, and SageMaker batch pipelines, and support these pipelines in production, diagnosing issues such as memory errors and capacity constraints.
• Cross-Functional Collaboration : Partner with platform, infrastructure, and product engineering teams to translate requirements into reliable, scalable ML systems, and collaborate alongside legal and compliance stakeholders to implement data governance frameworks.
• Technical Communication : Act as a point of contact for cross-team questions about de-identification behavior, communicating results and architectural decisions clearly in writing for technical, legal, and customer-facing audiences.

Basic Qualifications

• Experience: 5+ years of hands-on experience in Machine Learning Engineering or Data Science (or equivalent research experience via a Ph.D.).
• Core Programming & ML Stack: Proficiency in Python and modern ML frameworks such as PyTorch or TensorFlow.
• Data Pipelines: Proven experience building and operating large-scale data processing pipelines using Spark or equivalent distributed frameworks.
• Cloud & Production: Hands-on experience deploying, scaling, and maintaining ML systems in production on AWS (or equivalent cloud platform).
• Education: Bachelor’s degree in Computer Science, Physics, Mathematics, or a related quantitative field (or equivalent practical experience).

Other Qualifications (Nice-to-Haves / Areas to Grow)

• Privacy & Anonymization: Practical experience or research background in de-identification, group anonymization, Differential Privacy, or synthetic data generation.
• Modeling & NLP: Experience with classification, Named-Entity Recognition (NER), transformer architectures, LLM fine-tuning using the Hugging Face ecosystem, and model inference optimization for GPU hardware.
• GenAI & Agent Systems: Exposure to agent execution, agent orchestration, or LLM evaluation frameworks (e.g., LangGraph, LangSmith).
• Responsible AI & Compliance: Understanding of Responsible AI practices (bias/fairness evaluation) and privacy regulatory frameworks such as GDPR and CCPA.
• Cross-Functional Communication: Ability to translate technical design decisions and privacy-utility trade-offs for legal, compliance, and product partners.

Workday Pay Transparency Statement (For EU Locations Only)

Listed below is the base salary range applicable to this position. Workday pay ranges (and the precise pay offered to the successful candidate) are based on a number of objective criteria such as relevant experience and skills, and educational qualifications, level of responsibility, demands of the role, work location and business need. As a part of the total compensation package, this role may be eligible for the Workday Bonus Plan or a role-specific commission/bonus, as well as annual refresh stock grants awarded by Workday Inc. For more information regarding Workday’s comprehensive benefits, . Primary Location Base Pay Range: €80,000 EUR - €120,000 EUR Ireland

Our Approach to Flexible Work

With Flex Work, we’re combining the best of both worlds: in-person time and remote. Our approach enables our teams to deepen connections, maintain a strong community, and do their best work. We know that flexibility can take shape in many ways, so rather than a number of required days in-office each week, we simply spend at least half (50%) of our time each quarter in the office or in the field with our customers, prospects, and partners (depending on role). This means you'll have the freedom to create a flexible schedule that caters to your business, team, and personal needs, while being intentional to make the most of time spent together. Those in our remote "home office" roles also have the opportunity to come together in our offices for important moments that matter.

Pursuant to applicable Fair Chance law, Workday will consider for employment qualified applicants with arrest and conviction records.

Workday is an Equal Opportunity Employer including individuals with disabilities and protected veterans.

Workday prohibits taking adverse action against any candidate or employee for reporting a possible violation of this policy, requesting one or more work accommodations, exercising a privacy right, or cooperating in an investigation in accordance with applicable law. Any employee who retaliates against a candidate or employee for doing so may be subject to disciplinary action, up to and including termination of employment, to the fullest extent allowable under applicable law.

If you require a reasonable accommodation, you may email [e-mail] , as far in advance as possible.

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