Principal Machine Learning Engineer

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 and 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.

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. If you want to lead the work that sets the benchmark for how a global AI platform protects sensitive data — responsibly and at scale — we'd like to meet you.

As Principal Machine Learning Engineer for anonymization, you will lead Danu’s newest area: building the capability that provides anonymized datasets for customer-accessible research and benchmarking. You will drive this area from the front. Part of this work is expected to run with external research partners specialising in differential privacy and formal privacy analysis; you will lead from Workday’s side, setting the technical direction, owning the interface into those engagements, and bringing results back into the platform. You will own the architecture that turns it into a platform other teams can consume. You will drive a dedicated group of engineers hiring alongside you, and act as Workday's technical authority on anonymization with Product Legal, compliance and executive stakeholders. Your First Six Months You will establish how Workday measures the privacy-utility trade-off across the techniques in play — differential privacy, group anonymization (k-anonymity, l-diversity, t-closeness), and synthetic data generation — against real research use cases. That evaluation standard is what the broader platform capability is built on, and what legal, compliance, and customers are asked to trust. Architecture and roadmap follow from it, and you will own both.

Technical Leadership of Anonymization: Own the technical vision, architecture, and roadmap for Workday’s anonymization capability, building on Danu’s de-identification foundation to deliver a platform serving research, benchmarking, synthetic data generation, and agent evaluation across the AI ecosystem. Applied Privacy Research : Lead applied research across these techniques and the ones that follow them, closing linkage-attack gaps and incubating approaches ahead of industry convergence. Hands-On Technical Depth: Build and