Machine Learning Engineer

Role Overview

Join Workday’s AI Core team as a Machine Learning Engineer. You’ll build AI-powered features using agentic AI, LLMs, and RAG to create tailored user experiences across Workday’s product ecosystem.

Responsibilities

• Collaborate with other engineers to deliver machine learning solutions across Workday products.
• Build and own ML platforms, pipelines, and services, including training, deployment, and model lifecycle management.
• Drive exploration, design, and implementation of features for sophisticated ML infrastructure.
• Ensure evaluation, scalability, and observability of ML features.
• Apply ML methods (LLMs, natural language understanding) to analyze large HR and finance text datasets.
• Design and launch cloud-based ML architectures for production use.
• Act as a technical role model for junior engineers.

Requirements

• 3+ years building applied machine learning products at scale (end-to-end: applied research → design → implementation → production evaluation).
• Python expertise and strong software engineering skills; experience shipping production code and models.
• 3+ years cloud experience (e.g., AWS, GCP).
• 3+ years building information retrieval systems and/or graph-based recommendation systems.
• 3+ years hands-on experience with LLMs and/or text generation or graph-based ML in production (data processing, fine-tuning, deployment, evaluation).
• 3+ years building services to host ML models in production at scale.
• Experience with ML frameworks/toolkits: PyTorch, TensorFlow, scikit-learn, and PySpark.
• Data engineering/wrangling experience (e.g., Pandas, PySpark) and scalable tooling such as Kubernetes and Docker.
• Deep understanding of statistical analysis, supervised/unsupervised learning, and NLP for retrieval/recommendation.
• Ability to independently solve ambiguous, open-ended problems and technically lead teams.
• Bachelor’s degree (Master’s preferred; PhD preferred) in engineering, data/computer science, physics, math, or equivalent.

Nice to Have

• Advanced degrees (Master’s or PhD).