Principal, Machine Learning Software Engineer, Admin Central

Your work days are brighter here.

About the Team

Admin Central is building the future of how administrators work across Workday, turning a fragmented patchwork of tools and disconnected AI into a single, unified experience. Within Admin Central, Workday implementations move from being driven by human effort to being powered by intelligence designed into the platform. You’ll build mission-critical distributed systems, high-availability usage pipelines, new agentic functionality, and event-driven data flows across global product regions to ensure enterprise billing integrity at scale. If you're excited to build from scratch, move fast, and transform how thousands of admins work every day, come join us!

About the Role

As a Principal Software Engineer (ML/AI) on the Admin Central team, you will lead the architecture and implementation of Workday’s next-generation AI platforms and autonomous agent frameworks. You’ll sit at the intersection of machine learning, event-driven data systems, and enterprise platform engineering—transforming cutting-edge AI research into production-ready, highly reliable systems handling tens of millions of daily requests for hundreds of enterprise tenants.

In this hands-on, high-impact role, you will design the ML infrastructure, multi-agent orchestration layers, and high-availability usage pipelines that power enterprise-grade decision-making, usage tracking, and automatic tenant maintenance.

Your 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, AWS, and cloud-native tech. Architect multi-tenant runtimes that deliver 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/anonymization guardrails—ensuring systems are explainable, secure, and production-resilient.

• Technical & Strategic Leadership: Partner closely with software engineers, Product Managers, Data Engineering, SRE, and Legal/Compliance stakeholders to turn ambiguous business needs into scalable technical designs and long-term data-governance strategies.

About You

Basic Qualifications

• 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 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.

Other Qualifications

• Data Processing & Pipelines: Expert in high-throughput streaming (Kafka), real-time feature extraction (Spark/Flink), and distributed columnar storage (ClickHouse, Iceberg) to architect large-scale ML pipelines.
• AI/ML Expertise: 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) to multi-agent coordination, intent classification, and workflow optimization.
• Data Analysis & Feature Engineering: Proven ability to lead EDA on multi-tenant agentic workflows to shape data strategy, paired with mastery of high-dimensional embeddings, vector database management, and data isolation.
• Model Lifecycle: 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/latency at scale.
• Technical Stack: Mastery of Python and the ML ecosystem (PyTorch, TensorFlow, LangChain, LangSmith, LlamaIndex, NumPy, Pandas, Scikit-learn), with strong experience deploying production systems on PostgreSQL, Redis, Kafka, and cloud infrastructure (AWS/GCP/Azure).
• Team Leadership: Exceptional collaboration and mentoring skills — technically leading cross-functional pods, driving sprint planning, and translating technical/governance decisions for executives.
• Education: Bachelor's in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related field (or equivalent experience); Master's or PhD strongly preferred.

Workday Pay Transparency Statement

The annualized base salary ranges for the primary location and any additional locations are listed below.  Workday pay ranges vary based on work location. As a part of the total compensation package, this role may be eligible for the Workday Bonus Plan or a role-specific commission/bonus, as well as annual refresh stock grants. Recruiters can share more detail during the hiring process. Each candidate’s compensation offer will be based on multiple factors including, but not limited to, geography, experience, skills, job duties, and business need, among other things. For more information regarding Workday’s comprehensive benefits, please .

Primary Location: USA.CA.PleasantonPrimary Location Base Pay Range: $228,000 USD - $342,000 USD

 

Additional US Location(s) Base Pay Range: $190,600 USD - $342,000 USD

Our Approach to Flexible Work
 

With Flex Work, we’re combining the best of both worlds: in-person time and remote. Our approach enables our teams to deepen connections, maintain a strong community, and do their best work. We know that flexibility can take shape in many ways, so rather than a number of required days in-office each week, we simply spend at least half (50%) of our time each quarter in the office or in the field with our customers, prospects, and partners (depending on role). This means you'll have the freedom to create a flexible schedule that caters to your business, team, and personal needs, while being intentional to make the most of time spent together. Those in our remote "home office" roles also have the opportunity to come together in our offices for important moments that matter.