Machine Learning Engineer

About the role

Join Workday, a global technology company, as a Machine Learning Engineer in the AI Platform team. You will work on the Agent Evaluation Platform project, which is crucial for Workday's AI transformation. Your responsibilities will include designing and deploying sophisticated AI agents, developing algorithms for optimization, advancing information retrieval, scaling evaluation and observability, leading the ML lifecycle, and defining strategic roadmaps. You should have a strong background in machine learning, engineering, and collaboration. Key missions: 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.. 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. Profile: - 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 - We are seeking pragmatic ML Engineers to drive the applied research, deployment, and optimization of our Agentic AI, Search, and Semantic Parsing products - 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 - 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 - 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 - Collaborative Leadership: Demonstrated ability to lead cross-functional teams, mentor junior engineers, and solve ambiguous problems with high autonomy - Academic Foundation: Advanced degree (Master’s or Ph.D.) in a quantitative field or a strong portfolio of peer-reviewed research publications - 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) - Data Pipelines: Proficiency in large-scale data processing (PySpark, SQL)