Sr ML Engineer, Admin Central

Workday • Pleasanton, CA

4d • $163,000 - $288,000 • Hybrid

About The Position

Workday is hiring Senior Machine Learning Engineers across two teams. The Admin Central team is building a unified experience for administrators across Workday, using platform intelligence to streamline implementation and administrative work. Within Admin Central, the Business Process Optimize (BPO) team develops the underlying platform infrastructure, including distributed systems, high-availability usage pipelines, agentic functionality, and event-driven data flows across global product regions to support enterprise billing integrity.

The role involves designing, deploying, and maintaining end-to-end machine-learning pipelines; developing LLM-driven recommendation features and algorithms for agentic workflows; optimizing real-time inference services for sub-second responses in high-concurrency, multi-tenant environments; and adding observability to track model performance, drift, and execution efficiency. The engineer will work with backend engineers on data and feature-store architecture and partner with Product, Data Engineering, SRE, and Legal/Compliance to turn business and governance needs into scalable ML solutions.

Workday uses a flexible work approach that combines in-person and remote work. Employees spend at least half of their time each quarter in the office or in the field with customers, prospects, and partners, depending on the role. Remote home-office roles may also come together at Workday offices for important moments.

Requirements

• At least 8 years of experience researching, developing, and implementing advanced machine-learning models and techniques, including at least 4 years building, deploying, and operating production ML systems at scale.
• At least 5 years of experience and proficiency in deep learning, NLP, and GenAI/LLM orchestration.
• Experience with frameworks and libraries such as PyTorch, TensorFlow, Scikit-learn, LangChain, and LlamaIndex, and proficiency in Java, Go, Scala, or C++ for high-performance backend systems.
• Strong ability in distributed processing, including Spark, Flink, or Kafka, and high-throughput columnar or vector storage such as ClickHouse, Pinecone, or Milvus.
• Knowledge of RAG architectures, LLM fine-tuning, intent classification, recommendation engines, Model-Based Design, MCP tooling, and structured agentic execution paths.
• Experience applying ML to multi-agent orchestration, contextual reasoning, and workflow optimization.
• Experience leading exploratory data analysis on multi-tenant execution logs and strong feature-engineering skills for vector embeddings, feature stores, and multi-tenant data isolation.
• Deep understanding of the model development lifecycle and experience with MLOps platforms such as Kubeflow, MLflow, Airflow, or SageMaker for monitoring, latency tracking, and retraining at scale.
• Strong proficiency in Python and ML libraries including PyTorch, TensorFlow, NumPy, Pandas, Scikit-learn, LangChain, LangSmith, or DSPy.
• Experience deploying scalable ML systems in cloud-native production using Docker, Kubernetes, gRPC or REST APIs, and CI/CD, under high-availability and low-latency SLAs.
• Ability to mentor team members, drive cross-functional technical alignment, draft system design specifications and model cards, and communicate technical and governance decisions.
• Bachelor’s degree in Computer Science, Data Science, Machine Learning, Applied Mathematics, Statistics, or Engineering.

Nice To Haves

• Master’s degree or PhD.

Responsibilities

• Architect, deploy, and maintain end-to-end machine-learning pipelines, from distributed data ingestion and feature engineering through model training, evaluation, and production deployment.
• Design algorithms and LLM-driven recommendation features, including insights and next-best-action models, to optimize agentic workflows and tool usage.
• Optimize real-time model inference and scoring microservices for sub-second response times in high-concurrency, multi-tenant environments.
• Integrate telemetry and tracing into ML pipelines to monitor model performance, prompt and response drift, and execution efficiency.
• Collaborate with backend engineers to use big-data platforms for high-dimensional feature retrieval and analytics.
• Partner with Product Managers, Data Engineering, SRE, and Legal/Compliance to translate business and governance requirements into scalable ML solutions.
• Lead exploratory data analysis on multi-tenant execution logs to validate prompt and response accuracy and optimize tool usage.
• Lead model building, including foundation-model adaptation, prompt engineering, fine-tuning, and low-latency inference.
• Mentor team members, drive cross-functional technical alignment, draft system design specifications and model cards, and communicate technical and governance decisions.

Benefits

• The role may be eligible for the Workday Bonus Plan or a role-specific commission or bonus.
• Annual refresh stock grants may be included in the total compensation package.
• Workday provides a comprehensive benefits package; specific benefits are not detailed in the posting.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior