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 Agent Optimization & Evaluation, and Information Retrieval team.. We are the Optimization and "Ground Truth" engine for Workday’s AI transformation, building the critical infrastructure that empowers over 65% of the Fortune 500.About the Role
We are seeking pragmatic ML and Senior 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 (MLE III)
Basic Qualifications (Senior MLE)
Deep Technical ML Leadership: 6+ 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 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: Expert-level Python skills with a focus on modular library design, asynchronous patterns, and scalable system architecture (state management/error handling) for non-deterministic AI outputs.
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).
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).
Collaborative Leadership: Demonstrated ability to lead cross-functional teams, mentor junior engineers, and solve ambiguous problems with high autonomy.
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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: CAN.ON.Toronto
Additional Considerations:
If performed in Colorado, the pay range for this job is $171,600 - $257,400 USD based on min and max pay range for that role if performed in CO.The application deadline for this role is the same as the posting end date stated as below:
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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