Your work days are brighter here.
At Workday, it all began with a conversation over breakfast. When our founders met at a sunny California diner, they came up with an idea to revolutionize the enterprise software market. And when we began to rise, one thing that really set us apart was our culture. A culture which was driven by our value of putting our people first. And ever since, the happiness, development, and contribution of every Workmate is central to who we are. Our Workmates believe a healthy employee-centric, collaborative culture is the essential mix of ingredients for success in business. That's why we look after our people, communities and the planet while still being profitable. Feel encouraged to shine, however that manifests: you don't need to hide who you are. You can feel the energy and the passion, it's what makes us unique. Inspired to make a brighter work day for all and transform with us to the next stage of our growth journey? Bring your brightest version of you and have a brighter work day here.
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About the Team
The Data Platform and Observability Engineering team, located in Pleasanton, CA; Boston, MA; Atlanta, GA and Dublin, Ireland, is vital to enabling real-time insights across Workday's platforms, infrastructure, and applications. We're committed to developing large-scale distributed data systems that support crucial Workday applications.Our team provides software for the collection, ingestion, storage, analytics, and visualization of critical data assets. We lead hundreds of terabytes of data, encapsulating billions of messages produced daily by Workday applications and services.
We are looking for an Analytics AI/ML Engineer who combines deep technical expertise in machine learning with strong engineering fundamentals. This engineer will play a crucial role in building and deploying AI/ML solutions that transform our operational data into intelligent insights. If you have a passion for developing production-ready ML systems and the technical skills to build scalable AI solutions from the ground up, we would love to hear from you.
About the Role
Design and implement machine learning models for anomaly detection, predictive analytics, and root cause analysis to support faster insights for Workday's systems observability
Develop correlation engines using complex ML algorithms to identify patterns and relationships in sophisticated multi-dimensional datasets
Create and optimize feature engineering pipelines that transform raw observability data into actionable ML features
Implement and deploy ML models for service outage prediction, performance optimization
Connecting data to the actionable model output that will help with reducing our MTTD/MTTR
Build intelligent agentic systems that can autonomously monitor, diagnose, and recommend remediation actions for system health and performance issues
Design and implement LLM-powered solutions for automated log analysis, incident analysis, and natural language querying of observability data
Collaborate with data engineers to design ML-optimized data schemas and storage solutions
Develop real-time inference systems that can process high-velocity streaming data with low latency
Research and prototype next-generation AI techniques including deep learning, time series forecasting, and unsupervised learning methods
Work closely with analytics leaders and product teams to translate business requirements into technical ML solutions
Contribute to the development of internal ML platforms and tools that enable self-service AI capabilities
About You
Basic Qualifications
3+ years of hands-on experience building and deploying machine learning models in production environments
5+ years of software engineering experience with focus on data-intensive applications
Bachelor's degree in Computer Science, Machine Learning, Data Science, or equivalent work experience
Strong programming skills in Python and experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn)
Experience with big data processing frameworks (Spark, Kafka) and distributed computing
Other Qualifications
Proven experience with MLOps tools and practices (MLflow, Kubeflow, Docker, Kubernetes)
Tried understanding of statistical analysis, time series analysis, and anomaly detection techniques
Experience with real-time ML inference systems and streaming data processing
Familiarity with cloud ML services (AWS SageMaker, GCP AI Platform, Azure ML)
Experience with feature stores, model registries, and ML experiment tracking
Knowledge of data visualization and monitoring tools (Grafana, Prometheus, Superset)
Strong understanding of software engineering standard methodologies including version control, testing, and CI/CD
Proven business acumen to identify opportunities where AI/ML can drive operational efficiency and incident triaging
Experience with SQL and NoSQL databases for ML feature storage and retrieval
Excellent interpersonal skills to collaborate with multi-functional teams and explain technical concepts to non-technical collaborators
Self-motivated with ability to work independently and drive projects to completion
Workday Pay Transparency Statement
The annualized base salary ranges for the primary location and any additional locations are listed below. Workday pay ranges vary based on work location. 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. Recruiters can share more detail during the hiring process. Each candidate's compensation offer will be based on multiple factors including, but not limited to, geography, experience, skills, job duties, and business need, among other things. For more information regarding Workday's comprehensive benefits, please click here.
Primary Location: USA.CA.Pleasanton
Our Approach to Flexible Work
With Flex Work, we're combining the best of both worlds: in-person time and remote. Our approach enables our teams to deepen connections, maintain a strong community, and do their best work. We know that flexibility can take shape in many ways, so rather than a number of required days in-office each week, we simply spend at least half (50%) of our time each quarter in the office or in the field with our customers, prospects, and partners (depending on role). This means you'll have the freedom to create a flexible schedule that caters to your business, team, and personal needs, while being intentional to make the most of time spent together. Those in our remote "home office" roles also have the opportunity to come together in our offices for important moments that matter.
Pursuant to applicable Fair Chance law, Workday will consider for employment qualified applicants with arrest and conviction records.
Workday is an Equal Opportunity Employer including individuals with disabilities and protected veterans.
Are you being referred to one of our roles? If so, ask your connection at Workday about our Employee Referral process!