Principal Machine Learning Engineer

Principal Machine Learning Engineer For Anonymization

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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.

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.

Key Responsibilities

• 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 evaluate the anonymization models — calibrating privacy parameters against utility, and running the adversarial evaluations that test whether the guarantees hold.

• Leading the Group & Setting Standards: Drive the anonymization group day to day — technical planning, design review, and code review. Establish the architectural patterns for anonymization, aligned with the de-identification standards Danu already operates, and define what partner teams build against when they consume anonymized data.

• Strategic Collaboration: Partner with Product Legal, compliance, product, executive stakeholders and external partners to shape Workday's long-term anonymization and data-governance strategy, and translate privacy-utility decisions for technical, legal, and customer-facing audiences.

• External Representation: Represent Workday's anonymization work externally — benchmarking against emerging privacy frameworks, engaging with the research community, and contributing to the standards the industry is still forming.

You are a technical authority in machine learning with a track record of taking hard problems from research concepts to enterprise-grade production, and of leading others while staying close to engineering. You combine depth in privacy-preserving techniques with the judgement to make defensible trade-offs where the literature offers no clear answer, and the communication skill to explain those trade-offs to people who are not engineers.

Basic Qualifications

• Experience: 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.

• Privacy & Anonymization: Demonstrable depth in privacy-preserving machine learning, in research or production — differential privacy, group anonymization, or synthetic data generation. Given how recently these techniques have matured, we are looking for genuine expertise rather than long tenure.

• Core Programming & ML Stack: Expert-level Python and modern ML frameworks such as PyTorch or TensorFlow.

• Data Pipelines: Proven experience architecting and operating large-scale data processing pipelines using Spark or equivalent distributed frameworks.

• Technical Leadership: Track record of driving a group of engineers through ambiguous technical problems from a standing start, setting cross-team architecture standards, and mentoring mid-level and senior engineers.

• 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)

• 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.

• Advanced Degree: Master's or Ph.D. in Computer Science, Machine Learning, Statistics, or a related quantitative discipline.

• Cloud & Production: Hands-on experience deploying, scaling, and maintaining ML systems in production on AWS (or equivalent cloud platform).

• GenAI & Agent Systems: Experience with agent execution, agent orchestration, or LLM evaluation frameworks (e.g. LangGraph, LangSmith), particularly where agents consume sensitive data at scale.

• Responsible AI & Compliance: Strong understanding of Responsible AI practices (bias/fairness evaluation) and privacy regulatory frameworks such as GDPR, with experience representing technical decisions to legal and compliance partners.

• Working with External Partners: Experience directing external research partners, consultancies, or academic collaborators — scoping the work, assessing methodology critically, and bringing results into production systems.

• Research Visibility: Publications, patents, open-source contributions, or conference work in privacy-preserving ML or adjacent fields.