Principal, Machine Learning Software Engineer, Admin Central

Workday • Pleasanton, CA

19h • Hybrid

About The Position

Workday’s Admin Central team is developing a unified experience for administrators across Workday, incorporating intelligence into implementation workflows. The Principal Software Engineer (ML/AI) will lead the architecture and implementation of next-generation AI platforms and autonomous agent frameworks. The role combines machine learning, event-driven data systems, and enterprise platform engineering to turn AI research into reliable production systems serving tens of millions of daily requests across enterprise tenants.

This hands-on role involves designing ML infrastructure, multi-agent orchestration, and highly available usage pipelines for enterprise decision-making, usage tracking, and automated tenant maintenance. The engineer will build secure, high-throughput services and data pipelines, establish MLOps practices, and collaborate with engineering, product, data, reliability, and legal/compliance partners on scalable technical designs and data governance.

Requirements

• 12+ years in ML development and R&D leadership, including 8+ years with advanced ML methodologies, distributed frameworks, and cloud-based platforms, designing complex, scalable, production-grade ML systems.
• 6+ years building and hosting scalable ML services, microservices, and high-throughput production data pipelines 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 such as Kafka, real-time feature extraction using Spark or Flink, and distributed columnar storage such as ClickHouse or Iceberg.
• Deep knowledge of deep learning, LLM orchestration, RAG, and agentic systems, including ML algorithms, Model-Based Design, MCP integrations, and tool-use frameworks for multi-agent coordination, intent classification, and workflow optimization.
• Proven ability to lead exploratory data analysis on multi-tenant agentic workflows and mastery of 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 management of drift and latency at scale.
• Mastery of Python and the ML ecosystem, including PyTorch, TensorFlow, LangChain, LangSmith, LlamaIndex, NumPy, Pandas, and Scikit-learn.
• Strong experience deploying production systems on PostgreSQL, Redis, Kafka, and cloud infrastructure such as AWS, GCP, or Azure.
• Exceptional collaboration and mentoring skills, including technical leadership of cross-functional pods, sprint planning, and communication of technical and governance decisions to executives.
• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field, or equivalent experience.

Nice To Haves

• Master’s degree or PhD.

Responsibilities

• Design, implement, and maintain agentic AI 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, AWS, and cloud-native technologies.
• Architect multi-tenant runtimes that provide fast, low-latency inference to global users.
• Establish end-to-end MLOps standards, including CI/CD automation, observability, evaluation frameworks, model monitoring, and privacy and anonymization guardrails.
• Lead architecture and implementation of AI platforms, ML infrastructure, multi-agent orchestration layers, and high-availability usage pipelines.
• Partner with software engineers, Product Managers, Data Engineering, SRE, and Legal/Compliance stakeholders to translate ambiguous business needs into scalable technical designs and long-term data-governance strategies.
• Architect large-scale ML pipelines using high-throughput streaming, real-time feature extraction, and distributed columnar storage.
• Lead exploratory data analysis on multi-tenant agentic workflows to shape data strategy.
• Lead model building, fine-tuning, and deployment of domain-specific foundation models, and manage model drift and latency at scale.
• Technically lead cross-functional pods, drive sprint planning, mentor others, and communicate technical and governance decisions to executives.

Benefits

• The role may be eligible for the Workday Bonus Plan or a role-specific commission/bonus.
• Annual refresh stock grants may be part of the total compensation package.
• Flexible work schedule with at least half of each quarter spent in the office or in the field with customers, prospects, and partners, depending on the role.

What This Job Offers

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