Principal Machine Learning Engineer, Business Process Optimization

The Principal Machine Learning Engineer will lead architecture and implementation work on next-generation AI capabilities for Workday’s Business Process Optimize team within Admin Central. This role focuses on agentic frameworks, multi-tenant ML infrastructure, and enterprise-grade reliability to support billing integrity across global regions.

Role Focus

• Agentic AI & Orchestration: Design, implement, and maintain advanced agentic capabilities, including tool calling, multi-agent workflows, long-range planning, reasoning frameworks, and human-in-the-loop integrations.
• Scalable ML Infrastructure: Build secure, high-throughput REST services and event-driven data pipelines using Python, Kubernetes, and cloud-native technologies. Architect multi-tenant runtimes for fast, low-latency inference to millions of global users.
• MLOps & Enterprise Reliability: Establish end-to-end MLOps standards, including CI/CD automation, observability, evaluation frameworks, model monitoring, and privacy or anonymization guardrails. Ensure systems remain explainable, secure, and production-resilient.
• Technical & Strategic Leadership: Collaborate with software engineers, Product Managers, Data Engineering, SRE, and Legal or Compliance stakeholders to convert ambiguous business needs into scalable technical designs and long-term data-governance strategies.

Key Responsibilities

• Design, implement, and maintain advanced agentic capabilities, including tool calling, multi-agent workflows, long-range planning, reasoning frameworks, and human-in-the-loop integrations.
• Build secure, high-throughput REST services and event-driven data pipelines using Python, Kubernetes, and cloud-native technologies.
• Architect multi-tenant runtimes that deliver fast, low-latency inference to millions of global users.
• Establish end-to-end MLOps standards, including CI/CD automation, observability, evaluation frameworks, model monitoring, and privacy or anonymization guardrails, ensuring systems are explainable, secure, and production-resilient.
• Partner with software engineers, Product Managers, Data Engineering, SRE, and Legal or Compliance stakeholders to produce scalable technical designs and long-term data-governance strategies.

Required Qualifications

• 12+ years in ML development and R&D leadership, including 8+ years with advanced ML methodologies, distributed frameworks, and cloud-based platforms, delivering complex, scalable, production-grade ML systems.
• 6+ years building and hosting scalable ML services, microservices, and high-throughput data pipelines in production using Java, Go, Scala, or Python.
• 4+ years working with LLMs, generative AI, RAG pipelines, and autonomous agent frameworks for real-world enterprise applications.
• Expertise in high-throughput streaming (Kafka), real-time feature extraction (Spark/Flink), and distributed columnar storage (ClickHouse, Iceberg) to architect large-scale ML pipelines.
• Deep knowledge of deep learning, LLM orchestration, RAG, and agentic systems, applying ML algorithms and Model-Based Design (including MCP integrations and tool-use frameworks) for multi-agent coordination, intent classification, and workflow optimization.
• Proven ability to lead EDA on multi-tenant agentic workflows, including high-dimensional embeddings, vector database management, and data isolation.
• Proven ability to lead model building, fine-tuning, and deployment of domain-specific foundation models, including Kubernetes/Docker deployment, automated CI/CD retraining, and managing drift and latency at scale.
• Mastery of Python and the ML ecosystem (PyTorch, TensorFlow, LangChain, LangSmith, LlamaIndex, NumPy, Pandas, Scikit-learn) and strong experience deploying production systems on PostgreSQL, Redis, Kafka, and cloud infrastructure (AWS/GCP/Azure).
• Exceptional collaboration and mentoring skills, including technical leadership across cross-functional pods, driving sprint planning, and translating technical and governance decisions for executives.

Education

• Bachelor’s degree (or equivalent experience) in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
• Master’s or PhD strongly preferred.

Location and Work Model

• Pleasanton, CA (hybrid)

Compensation

• USD 190,600 - 342,000 per yearly

Workday Pay Transparency Statement

• Primary Location: USA.CA.Pleasanton
• Primary Location Base Pay Range: $228,000 USD - $342,000 USD
• Additional US Location(s) Base Pay Range: $190,600 USD - $342,000 USD

Flexible Work Approach

• Spend at least half (50%) of your time each quarter in the office or in the field with customers, prospects, and partners (depending on role).
• Remote “home office” roles may have opportunities to come together in Workday offices for important moments.

Technologies

• Python, Kubernetes
• Java, Go, Scala
• REST services, event-driven data pipelines
• CI/CD automation, observability, model monitoring
• Privacy and anonymization guardrails
• LLMs, generative AI, RAG pipelines
• Autonomous agent frameworks, tool calling, multi-agent workflows, long-range planning, reasoning frameworks, human-in-the-loop integrations
• Kafka, Spark, Flink
• ClickHouse, Iceberg
• MCP integrations, Model-Based Design, EDA
• High-dimensional embeddings, vector database management
• Docker
• PyTorch, TensorFlow
• LangChain, LangSmith, LlamaIndex
• NumPy, Pandas, Scikit-learn
• PostgreSQL, Redis, AWS, GCP, Azure