Principal Machine Learning Engineer

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.

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.

• →

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.

•    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).

•    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.

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, please click here.

At Workday, we value our candidates’ privacy and data security.  Workday will never ask candidates to apply to jobs through websites that are not Workday Careers. 

Please be aware of sites that may ask for you to input your data in connection with a job posting that appears to be from Workday but is not.

In addition, Workday will never ask candidates to pay a recruiting fee, or pay for consulting or coaching services, in order to apply for a job at Workday.