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.
About The TeamThis 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.
Our Mission- Agent Optimization & Evaluation: Providing the algorithms and rigorous data‑driven frameworks to validate, scale, and optimize AI agents across our entire enterprise suite.
- Information Retrieval: Developing the intelligence layer that bridges human language and enterprise data through advanced semantic search and natural language‑to‑code (SQL/Python) execution.
- The Data & Frontier: Solve unique challenges in Agentic AI using exclusive, high‑integrity enterprise datasets.
- Impact at Scale: Your work acts as the optimizer and gatekeeper of quality for products reaching 31 million users globally.
- People‑First Culture: We balance high‑intensity innovation with a commitment to sustainable work‑life integration.
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.
Responsibilities- Architect Agentic AI: Design and deploy sophisticated reasoning, planning, and swarm agents that interact seamlessly with enterprise data and support continuous, lifelong 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 of 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.
- 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 such as PyTorch or TensorFlow.
- Generative AI & Agentic Systems: 0.5+ years of 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: 0.5+ years expert‑level Python experience with a focus on modular library design, asynchronous patterns, and scalable system architecture (state management/error handling) for non‑deterministic AI outputs.
- 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 such as PyTorch or TensorFlow.
- Generative AI & Agentic Systems: 1+ years of 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: 1+ years of expert‑level Python experience with a focus on modular library design, asynchronous patterns, and scalable system architecture (state management/error handling) for non‑deterministic AI outputs.
- 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 such as 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.
Primary Location: CAN.ON.Toronto
Base Pay Range: $156,000 CAD – $234,000 CAD
Additional US Location(s) Range: $163,000 USD – $288,000 USD
Colorado: $171,600 – $257,400 USD
Pay is based on geography, experience, skills, job duties, and business need.
With Flex Work we combine the best of both worlds: in‑person time and remote. Our approach enables teams to deepen connections, maintain a strong community, and do their best work. Flexibility takes shape in many ways—we require at least half (50%) of our time each quarter in the office or in the field with customers, prospects, and partners, depending on the role. Those in our remote \"home office\" roles also have the opportunity to come together in our offices for important moments.
Equal Opportunity and Accommodation StatementWorkday is an Equal Opportunity Employer inclusive of individuals with disabilities and protected veterans. Pursuant to applicable Fair Chance law we consider qualified applicants with arrest and conviction records. We are committed to an accessible and inclusive hiring experience; if you require assistance or an accommodation at any point, please email .
#J-18808-Ljbffr