Sr. Software Development Engineer

Workday•Boulder, CO

23h•Hybrid

About The Position

Workday is seeking a Sr. Software Development Engineer for the AI Model Serving team. This team is responsible for the services that power all production AI workloads, acting as a gateway to vendor-hosted LLMs and the primary platform for hosting and scaling internal models. The team operates at scale, handling thousands of traditional ML models and maintaining Workday's production model registry, with a current throughput of approximately 2,000 requests per second, peaking at over 10,000 RPS. The engineering roadmap includes scaling the architecture to support new AI agents, hosting open-weight LLMs, optimizing performance and reliability, and implementing enterprise governance controls. The team culture is focused on camaraderie and high performance.

Requirements

6+ years of related work experience in software development, with a focus on building and operating large-scale distributed systems.
Bachelor's degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience).
Deep experience designing, building, and scaling production-grade distributed systems.
Understanding of 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.
Deep proficiency in Python, with extensive experience writing production-level code and building systems in Python-based frameworks.
Deep hands-on experience deploying and scaling workloads on Kubernetes, with a specific focus on GPU resource management.
Understanding of how to optimize GPU utilization for hosting and tuning smaller open-weight LLMs using modern inference engines (e.g., vLLM, TGI).
Familiarity with GPU memory constraints, serving tuned models (e.g., LoRA), and autoscaling hardware metrics.
Familiarity with both large language models and traditional ML models, including how they are served, scaled, and monitored in production.
Ability to design abstractions that serve both LLMs and traditional ML models effectively.
Ability to design and maintain monitoring strategies that provide clear insight into system health, performance, and cost.
Excellent written and verbal communication skills, including the ability to write clear design documents, articulate complex technical ideas, and build consensus across teams.
A collaborative approach to engineering, with experience mentoring other engineers and fostering an inclusive team environment.

Nice To Haves

Familiarity with LLMs and Traditional ML Models, including how they are served, scaled, and monitored in production.
Understanding of the operational differences between LLMs and traditional ML models.
Experience with modern inference engines (e.g., vLLM, TGI).
Experience with serving tuned models (e.g., LoRA).

Responsibilities

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.