- Company Name
- Workday
- Job Title
- Machine Learning Engineer - Agent Factory
- Job Description
-
Job Title: Machine Learning Engineer – Agent Factory
Role Summary: Design, build, and maintain production-grade ML systems for Workday’s AI Agent Factory. Own the full model lifecycle—from problem framing and data strategy to deployment, monitoring, and continuous improvement—within a small, senior cross‑functional pod. Bridge LLM-powered agents, RAG pipelines, workflow orchestration, and platform architecture to deliver scalable, observable, and enterprise‑ready solutions.
Expectations: • Deliver end‑to‑end ML solutions at scale, integrating responsibly with Workday’s platform. • Mentor and lead technical initiatives, fostering transparency, collaboration, and continuous improvement. • Stay abreast of AI advances (LLMs, RAG, autonomous agents) and drive innovation in applied research and production.
Key Responsibilities: • Own ML model design, training, and evaluation pipelines for LLM and graph‑based agents. • Engineer scalable inference services (model hosting, routing, scaling) on cloud platforms (AWS, GCP, etc.). • Implement RAG pipelines, workflow orchestration, and feedback loops for real‑time agent adaptation. • Monitor model performance, detect drift, and orchestrate retraining cycles. • Collaborate with product, software engineering, and data science teams to embed agents into the Workday stack. • Conduct statistical analysis, supervised/unsupervised modeling, NLP, and recommendation system development. • Lead sprint planning, development lifecycle ownership, and technology strategy. • Translate emerging research into production‑ready infrastructure with strong reliability and explainability.
Required Skills: • 5+ years ML engineering or applied ML product experience. • 2+ years in deep learning with PyTorch or TensorFlow. • 2+ years building scalable ML services (model serving, containerization, orchestration). • 2+ years hands‑on LLM and text generation model deployment. • 2+ years cloud platform experience (AWS, GCP, etc.). • Proficiency in NLP, statistical modeling, unsupervised and supervised ML. • Experience with RAG pipelines, autonomous agent architectures, and workflow orchestration. • Strong engineering judgment, ownership, and problem‑solving ability. • Excellent communication and leadership toward cross‑functional teams.
Required Education & Certifications: • Bachelor’s degree in Engineering, Computer Science, Physics, Mathematics, or equivalent; Master’s or PhD preferred. • Certifications in cloud platforms (AWS, GCP) or relevant ML frameworks are advantageous.
Pleasanton, United states
Hybrid
Mid level
27-01-2026