Machine Learning Engineer

# Machine Learning EngineerWorkdayIreland, DublinFull-timeAI tools:PyTorchTensorFlowHugging FaceLangGraphLangSmithUpload your resume once and Scout applies for youApply with ScoutShare this job:LinkedInXSave imageYou'll build and tune the models and data pipelines behind Workday's ogham de-identification engine and its planned anonymization capabilities. The work spans privacy technique evaluation, production ML operations, and collaboration with platform, product, infrastructure, legal, and compliance teams. Permanent 5+ years Bachelor's Degree or equivalent practical experience Skills and expertisePythonPyTorchTensorFlowSparkAWSAmazon EMRAmazon SageMakerNamed-Entity Recognition Key responsibilities Implement and evaluate anonymization techniques, measuring privacy-utility trade-offs on real use cases. Build and optimize de-identification models and production data pipelines. Partner across engineering, legal, and compliance teams to deliver scalable privacy capabilities. How this job comparesCompared with 410 open senior machine learning jobs on October 10, 2026.Similar open jobs: We Love AI Jobs lists 410 open senior machine learning jobs at 176 companies.Remote work: This job is on site. 90 of the 410 senior machine learning jobs (22%) are remote.AI in this role: High. AI is the core of the work: building, training or deploying AI systems. Building, fine-tuning, and evaluating machine-learning models and pipelines is the core of the role. The posting specifically calls out de-identification models, NER, and ML systems operating in production. Full descriptionYour 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.About the TeamDanu 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 help define how a global AI company earns the right to use sensitive data - responsibly, and at scale - we'd like to meet you.About the RoleAs 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 MonthsYou 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.About YouBasic 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.