Machine Learning Engineer III

Your work days are brighter here. We’re obsessed with making hard work pay off, for our people, our customers, and the world around us. As a Fortune 500 company and a leading AI platform for managing people, money, and agents, we’re shaping the future of work so teams can reach their potential and focus on what matters most.

The minute you join, you’ll feel it. Not just in the products we build, but in how we show up for each other. Our culture is rooted in integrity, empathy, and shared enthusiasm.

We’re in this together, tackling big challenges with bold ideas and genuine care. We look for curious minds and courageous collaborators who bring sun-drenched optimism and drive. Whether you're building smarter solutions, supporting customers, or creating a space where everyone belongs, you’ll do meaningful work with Workmates who’ve got your back.

In return, we’ll give you the trust to take risks, the tools to grow, the skills to develop and the support of a company invested in you for the long haul. So, if you want to inspire a brighter work day for everyone, including yourself, you’ve found a match in Workday, and we hope to be a match for you too. About the Team This is a very exciting opening in the AI Platform team in our Information Retrieval and Agent Evaluation team.

We believe if you do what you love, you’ll love what you do. There’s a lot to love at Workday. We are part of a global, high-growth technology company and our team has the opportunity to develop the next generation of Workday’s groundbreaking collaborative products supporting a customer base of more than 31 million strong.

Over 65% of the Fortune 500 are Workday customers. The Agent Evaluation Platform project is the "Ground Truth" engine for Workday’s AI transformation and we have an ambitious roadmap. As Workday infuses AI Agents into every facet of our enterprise suite, our team provides the critical infrastructure and algorithms needed to prove they work—and make them better.

We build the platform that enables agent engineering teams to be empowered with rigorous, data-driven optimization, evaluation and validation of their agents. The AI Platform Information Retrieval products are at the heart of Workday’s intelligence layer. We bridge the gap between human language, search, and enterprise data, including reasoning over knowledge.

Our products utilize advanced semantic search to navigate Workday’s massive data model, as well as turning natural language questions into precise SQL and Python executions. Workday’s AI Platform organization is bringing “AI first” products to life at every step of the Workday product offering. We’re looking for highly creative, results-focused, and deeply skilled Machine Learning Engineers/scientists to work with us on a range of these challenges.

Why Workday? 1. The Data: Work with exclusive, high-integrity enterprise datasets that most researchers never see.

You'll be working at the absolute frontier of Agentic AI - "how do we validate, scale, and optimize an agent" and "how do we extract the correct data for agents". 2. The Scale: Your code will empower the world’s largest companies to make data-driven decisions.

You are the gatekeeper of quality for products reaching 31 million users. 3. The Culture: A "people-first" environment that balances high-intensity innovation with sustainable work-life integration.

About the Role We are seeking pragmatic ML Engineers to drive the applied research, deployment, and optimization of our Agentic AI, Search, and Semantic Parsing products. In this role, you will bridge the gap between deep research and production, embedding cutting-edge agents directly into the Workday ecosystem. Leveraging our vast computing power and exclusive datasets, you will solve complex technical challenges to deliver transformative value to millions of users.

If you are ready to apply creative problem-solving to global-scale ML systems, we want to hear from you. In this role, you would: Architect Agentic AI: Design and deploy sophisticated reasoning, planning, and swarm agents that interact seamlessly with enterprise data and support continuous, life-long learning. Drive Meta-ML & Optimization: Develop algorithms for automated node-level optimization within agent graphs, identifying the best LLM and prompt configurations for every workflow step.

Build recommender systems for engineering teams to drive optimal evaluation for their agents. Advance Information Retrieval: Build hybrid, agentic search systems and semantic parsing products (Text-to-SQL/Python) utilizing vector search, reasoning, and fine-tuning for structured output. Scale Evaluation & Observability: Engineer cloud-based pipelines (Kubeflow) and A/B testing frameworks for rigorous offline/online evaluation, failure attribution, and safety monitoring.

Lead the ML Lifecycle: Own the end-to-end MLOps process—from exploration and prompt engineering to scalable production deployment—ensuring high-quality, reliable performance. Define Strategic Roadmaps: Independently identify ML opportunities, propose high-impact solutions to leadership, and integrate industry best practices across the organization. Collaborate with Autonomy: Work cross-functionally with PMs and Engineers to deliver "AI-first" products, enjoying full ownership of your work within a supportive, growth-oriented culture.

About You Basic Qualifications: Deep Technical ML Capability: 3+ years of experience researching, developing and deploying production-grade ML systems, including expertise in deep learning, NLP, Information Retrieval, and recommender systems using frameworks like PyTorch or TensorFlow. Generative AI & Agentic Systems: Proven track record of building and evaluating NLP and LLM-powered products, including expertise in RAG architectures, agentic frameworks (e.g., LangChain/LangGraph), and long-context LLM applications (e.g., Text-to-SQL). Engineering Excellence: 2+ years of Python experience with a focus on modular library design, asynchronous patterns, and scalable system architecture (state management/error handling) for non-deterministic AI outputs.

Other Qualifications Academic Foundation: Advanced degree (Master’s or Ph.D.) in a quantitative field or a strong portfolio of peer-reviewed research publications. Optimization & Advanced Techniques: Proficiency in techniques like DSPy, Reinforcement Learning, imitation learning, graph neural networks, multi-modal models, and large-scale data processing (PySpark, SQL). Experimental Rigor: A "test-everything" mindset with experience in A/B testing, Knowledge Graphs, and "Golden Dataset" curation for model benchmarking.

Data Pipelines: Proficiency in large-scale data processing (PySpark, SQL). Production MLOps: Hands-on experience with the full ML lifecycle, including model fine-tuning (PEFT), evaluation frameworks (e.g., DeepEval/RAGAS), and cloud-native deployment (Docker/K8s, AWS/GCP). Collaborative Leadership: Demonstrated ability to lead cross-functional teams, mentor junior engineers, and solve ambiguous problems with high autonomy.

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 click here .

Primary Location: USA.CA.Pleasanton Primary Location Base Pay Range: $160,000 USD - $240,000 USD Additional US Location(s) Base Pay Range: $136,200 USD - $240,000 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. Pursuant to applicable Fair Chance law, Workday will consider for employment qualified applicants with arrest and conviction records.

Workday is an Equal Opportunity Employer including individuals with disabilities and protected veterans. At Workday, we are committed to providing an accessible and inclusive hiring experience where all candidates can fully demonstrate their skills. If you require assistance or an accommodation at any point, please email accommodations@workday.com .

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