Principal Machine Learning Engineer, Workday

• 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).

Desirable

• 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
• 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
• 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