Senior Software Development Engineer

Join Workday's AI Model Serving team as a Senior Software Development Engineer. You will be a technical leader, shaping the vision and direction of the platform, making critical design decisions, and driving outcomes across the team. Your work will directly impact Workday's ability to serve AI at scale, from traditional ML models to the latest large language models. You will design, implement, and maintain large-scale systems, troubleshoot production issues, and develop relationships with partner teams.

• 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.
• Develop relationships with software engineers, machine learning engineers, and data scientists on partner teams.

• 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)
• Communication: Excellent written and verbal communication skills, including the ability to write clear design documents, articulate complex technical ideas, and build consensus across teams
• Python: Deep proficiency in Python, with extensive experience writing production-level code and building systems in Python-based frameworks
• 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
• 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, TGI). Familiarity with GPU memory constraints, serving tuned models (e.g., LoRA), and autoscaling hardware metrics
• Observability: You can design and maintain monitoring strategies that provide clear insight into system health, performance, and cost
• 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
• Mentorship: A collaborative approach to engineering, with experience mentoring other engineers and fostering an inclusive team environment