Senior Machine Learning Engineer

Workday as a Senior Machine Learning Engineer on the AI Model Serving team. In this role, you will be a technical leader, making critical design decisions and driving outcomes across the team. Your work will directly impact Workday's ability to serve AI at scale.

The ideal candidate will have deep experience in software development, distributed systems, and Python, as well as excellent communication skills and a collaborative approach to engineering.

Lead the technical direction of the AI Model Serving team, making critical design decisions that drive performance, reliability, and scalability across the platform.

About Workday

Mentorship: A collaborative approach to engineering, with experience mentoring other engineers and fostering an inclusive team environment

Responsibilities

• You will design, implement, and maintain large-scale systems, troubleshoot and improve continuous integration software pipelines, and develop relationships with partner teams.
• Design, implement, and maintain large-scale systems that enable moving machine learning models to production, ensuring high throughput and low latency.
• Collaborate with cross-functional teams, including software engineers, machine learning engineers, and data scientists, to build consensus for new system components and enhancements.
• Kubernetes & GPU Infrastructure: Deep hands-on experience deploying and scaling workloads on Kubernetes, with a specific focus on GPU resource management. You understand how to optimize GPU utilization for hosting and tuning smaller open-weight LLMs using modern inference engines (e.g., vLLM, SGLang). Familiarity with GPU memory constraints, serving tuned models (e.g., LoRA), and autoscaling hardware metrics

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience)
• 6+ years of related work experience in software development, with a focus on building and operating large-scale distributed systems
• Software Development and Distributed Systems: Deep experience designing, building, and scaling production-grade distributed systems. You understand the full software development lifecycle - from coding standards and testing to code reviews, source control, and deployment, and can apply that knowledge to complex, high-throughput platforms
• LLMs and Traditional ML Models: Familiarity with both large language models and traditional ML models, including how they are served, scaled, and monitored in production. You understand the operational differences and can design abstractions that serve both effectively
• Python: Deep proficiency in Python, with extensive experience writing production-level code and building systems in Python-based frameworks
• Communication: Excellent written and verbal communication skills, including the ability to write clear design documents, articulate complex technical ideas, and build consensus across teams
• Observability: You can design and maintain monitoring strategies that provide clear insight into system health, performance, and cost