Machine Learning Engineer / Senior Machine Learning Engineer (AI Platform)

Role Overview

Join Workday’s AI Platform team as a Machine Learning Engineer or Senior Machine Learning Engineer. You’ll build and deploy AI systems focused on agent optimization, evaluation, and information retrieval, driving the end-to-end ML lifecycle from experimentation to production.

Key Missions

• Architect & deploy agentic AI: reasoning, planning, and swarm agents that interact with enterprise data and support continuous learning.
• Automate optimization in agent graphs: develop algorithms for node-level optimization and identify the best LLM + prompt configurations per workflow step.
• Own end-to-end MLOps: manage the full lifecycle from exploration and prompt engineering to scalable production deployment, ensuring reliable, high-quality performance.
• Scale evaluation & observability: advance evaluation frameworks, monitoring, and observability for ML systems.
• Drive meta-ML and roadmapping: lead ML lifecycle execution and define strategic technical roadmaps.

Responsibilities

• Lead the design and delivery of production-grade ML systems with a strong focus on evaluation, reliability, and system architecture.
• Support continuous improvement via benchmarking, test rigor, and experimentation.
• Collaborate cross-functionally and mentor engineers.

Requirements

• Python: 0.5+ years (MLE) or 1+ years (Senior) of expert-level Python; experience with modular library design, asynchronous patterns, and scalable system architecture (state management/error handling) for non-deterministic AI.
• Generative AI & agentic systems: 0.5+ years (MLE) or 1+ years (Senior) building and evaluating NLP/LLM products.
  • RAG architectures
  • Agent frameworks such as LangChain/LangGraph
  • Long-context LLM applications (e.g., Text-to-SQL)
• Deep technical ML:
  • 3+ years (MLE) researching/deploying production ML; expertise in deep learning, NLP, Information Retrieval, and recommender systems using PyTorch or TensorFlow.
  • 6+ years (Senior) for deeper leadership and production impact.
• Data & pipelines: proficiency in large-scale data processing (PySpark, SQL).
• Optimization & advanced techniques: experience with tools/approaches such as DSPy, Reinforcement Learning, imitation learning, Graph Neural Networks, multi-modal models, and large-scale processing.
• MLOps / production readiness: full ML lifecycle including PEFT, evaluation frameworks (DeepEval/RAGAS), and cloud-native deployment (Docker/Kubernetes, AWS/GCP).
• Experimental rigor: “test-everything” mindset; A/B testing, Knowledge Graphs, and Golden Dataset curation for benchmarking.
• Education: advanced degree (Master’s/Ph.D.) in a quantitative field or strong peer-reviewed research portfolio.

Nice-to-haves

• Demonstrated ability to lead cross-functional teams and solve ambiguous problems with high autonomy.