Workday’s Business Process Optimize team within Admin Central is hiring a Principal Machine Learning Engineer to lead the architecture and implementation of next-generation AI platforms and autonomous agent frameworks. The role focuses on ML infrastructure, multi-agent orchestration, and reliable usage pipelines that support enterprise billing integrity across global regions.
Primary 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 that provide fast, low-latency inference to millions of global users.
- MLOps & Enterprise Reliability: Establish end-to-end MLOps standards with CI/CD automation, observability, evaluation frameworks, model monitoring, and privacy or anonymization guardrails. Ensure systems remain explainable, secure, and resilient in production.
- 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.
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 technology.
- Architect multi-tenant runtimes that deliver fast, low-latency inference to millions of global users.
- Establish end-to-end MLOps standards covering CI/CD automation, observability, evaluation frameworks, model monitoring, and privacy or anonymization guardrails, with a focus on explainability, security, and production reliability.
- Partner with cross-functional teams across engineering, product, data engineering, SRE, and Legal or Compliance to develop scalable designs and long-term data-governance strategies.
Requirements
- 12+ years of 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 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 or Flink), and distributed columnar storage (ClickHouse or Iceberg) to design 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.
- Demonstrated ability to lead EDA on multi-tenant agentic workflows, including mastery of high-dimensional embeddings, vector database management, and data isolation.
- Demonstrated ability to lead model building, fine-tuning, and deployment of domain-specific foundation models, including Kubernetes or 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) with production experience deploying on PostgreSQL, Redis, Kafka, and cloud infrastructure (AWS, GCP, or Azure).
- Exceptional collaboration and mentoring skills, including technically leading cross-functional pods, driving sprint planning, and translating technical and governance decisions for executives.
- Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field (or equivalent experience); Master’s or PhD strongly preferred.
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, distributed frameworks, cloud-based platforms, 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.
Role Location and Work Approach
- Location: Pleasanton, CA (hybrid)
- Office attendance: 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: Those in remote “home office” roles may come together in offices for important moments that matter.
Compensation
- Salary range: 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
