Machine Learning Engineer III / Senior Machine Learning Engineer - AI Platform

Workday•Boulder, CO

1d•Hybrid

About The Position

This is an exciting opening in the AI Platform team within the 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 is two-fold: 1. Agent Optimization & Evaluation: Providing the algorithms and rigorous data-driven frameworks to validate, scale, and optimize AI agents across our entire enterprise suite. 2. 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. We are looking for creative, results-focused ML Engineers and Senior ML Engineers to help us build the next generation of "AI-first" products. 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.

Requirements

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: 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 like 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.

Nice To Haves

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.

Responsibilities

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.