Senior Machine Learning Engineer
Posted Updated
Description
- As a Senior Machine Learning Engineer on the AI Model Serving team, you will be a technical leader who helps shape the vision and direction of the platform
- You will play a central role in making critical design decisions, driving outcomes across the team, and setting a positive and inclusive team culture
- Your work will directly impact Workday’s ability to serve AI at scale — from traditional ML models to the latest large language models powering Workday’s agents
- Lead the team technically by making critical design decisions that drive performance, reliability, and scalability across the platform
- Design, implement, and maintain large-scale systems that enable moving ML models to production
- Write design documents to build consensus for new system components and enhancements to existing components
- Evaluate and uptake new technologies made available within Workday and across the broader industry
- Troubleshoot, improve, and scale continuous integration software pipelines
- Develop relationships with software engineers, machine learning engineers, and data scientists on partner teams
- Respond to alerts and debug production issues to maintain platform health and reliability
- Review pull requests and enforce consistency, performance, readability, and security across code bases
- Develop documentation to share knowledge with other engineers
- 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
- 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
- Mentorship: A collaborative approach to engineering, with experience mentoring other engineers and fostering an inclusive team environment
- End Date: October 30, 2026