Sr. Machine Learning Engineer

Workday; Boulder (CO); On-site; USD 140,000 - 190,000; Full time

Job summary

Workday is seeking a Senior Machine Learning Engineer to lead the AI Model Serving team. You will shape the platform vision, drive critical design choices, and foster a positive, inclusive culture while delivering scalable ML model serving for Workday’s AI workloads.

You will collaborate with software engineers, data scientists, and partner teams to move models to production, from traditional pipelines to open-weight LLMs, ensuring reliability and performance at scale.

Qualifications

• Experience building and operating large-scale distributed systems.
• Strong Python production code and systems experience.
• Experience with ML model serving and monitoring in production.

Responsibilities

• Lead design decisions for scalable ML model serving platforms.
• Design, implement, and maintain large-scale systems for production ML models.
• Write design documents to align stakeholders on new components.
• Evaluate new technologies and improve CI pipelines.
• Collaborate with engineers, data scientists, and other teams.
• Respond to alerts and maintain platform reliability.
• Review PRs for quality, performance, and security.

Skills

Distributed systems

Python

ML model serving

Observability

Communication

Mentorship

Education

Bachelor's degree in CS/Engineering or related field

Tools

Kubernetes

GPU infrastructure

Job description

Your work days are brighter here. We’re obsessed with making hard work pay off, for our people, our customers, and the world around us. As a Fortune 500 company and a leading AI platform for managing people, money, and agents, we’re shaping the future of work so teams can reach their potential and focus on what matters most. The minute you join, you’ll feel it. Not just in the products we build, but in how we show up for each other. Our culture is rooted in integrity, empathy, and shared enthusiasm. We’re in this together, tackling big challenges with bold ideas and genuine care. We look for curious minds and courageous collaborators who bring sun-drenched optimism and drive. Whether you're building smarter solutions, supporting customers, or creating a space where everyone belongs, you’ll do meaningful work with Workmates who’ve got your back. In return, we’ll give you the trust to take risks, the tools to grow, the skills to develop and the support of a company invested in you for the long haul. So, if you want to inspire a brighter work day for everyone, including yourself, you’ve found a match in Workday, and we hope to be a match for you too.

About the Team

The AI Model Serving team is the engine behind every production Workday agent and machine learning use case. We own the services that power all production AI workloads, acting as both the gateway to vendor-hosted LLMs (GCP, AWS Bedrock, Gemini) and the primary platform where Workday hosts and scales its internal models. We operate at scale, hosting thousands of traditional ML models across sharded Ray Serve clusters and maintaining Workday's production model registry.

In the year ahead, our engineering roadmap is highly ambitious. We are focused on: Scaling Architecture: Upgrading our systems to seamlessly support 20+ new AI agents going into production. Hosting Open-Weight LLMs: Designing the infrastructure to host and tune open-source LLMs directly within our stack. Performance & Reliability: Architecting optimizations to drive down core latency while maintaining the rock-solid stability our high-throughput production systems demand. Enterprise Governance: Hardening our unified vendor interface and implementing advanced cost-governance controls.

Our culture is built on focus, camaraderie, and high performance. We are a friendly, dedicated group that takes pride in building and operating one of the most heavily used services at Workday.

If you are energized by working on the infrastructure that sits at the very heart of Workday's AI strategy, this is the team for you.

About the Role

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.

Key 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.

About You

Basic Qualifications

• 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).
• 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.

Other Qualifications

• 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.
• Python: Deep proficiency in Python, with extensive experience writing production-level code and building systems in Python-based frameworks.
• 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.
• Observability: You can design and maintain monitoring strategies that provide clear insight into system health, performance, and cost.
• Communication: Excellent written and verbal communication skills, including the ability to write clear design documents, articulate complex technical ideas, and build consensus across teams.
• Mentorship: A collaborative approach to engineering, with experience mentoring other engineers and fostering an inclu