Senior Software Development Engineer

Workday•Boulder, CO

1d•Hybrid

About The Position

We're an AI platform organization. We build the platform other engineering teams use to develop, evaluate, and deploy AI systems, and we build production applications on top of it — agentic solutions alongside a broad range of classical ML. The work spans the full applied-AI stack: model and agent development, evaluation infrastructure, serving performance and cost, and the pipelines that get it all into an enterprise product. Our systems run against real customer data, so correctness, reliability, and observability matter as much as capability. We're hiring at senior software engineer to have someone own a significant surface area end to end — design through delivery and operation. As a Senior Software Development Engineer on the AI Foundations team, you will be primarily responsible for designing, building, and operating the software systems that host, run, and scale AI-powered applications at Workday. Specifically, you will: Work closely with machine learning engineers to write and maintain production-grade backend services that power AI-driven capabilities and agent applications Design and implement APIs and service integrations that enable AI capabilities to be consumed across Workday products and platforms Build and operate data ingestion and ETL pipelines that support AI application workflows Apply distributed systems principles in production to address scalability, concurrency, fault tolerance, and performance challenges Ensure systems meet enterprise requirements for security, privacy, robustness, and compliance Own services through their full lifecycle, including deployment, monitoring, debugging, and ongoing operational improvements

Requirements

8+ years of professional software development experience, including architecting, building, and scaling secure, robust, and efficient software systems
5+ years of experience with Python development
Bachelor’s degree in Computer Science, Engineering, or related discipline, or equivalent practical experience.
Understanding of object-oriented design principles and ability to apply them in a Python context
Proficiency with advanced Python concepts, such as asynchronous and concurrent programming, generators, and higher-order abstractions
Ability to write clean, testable, and well-structured code, with high standards for clarity, aesthetics, and long-term maintainability
Deep systems knowledge, including comfort operating in and debugging Unix/Linux environments, fluency with command-line tooling, and understanding of practical networking fundamentals
Understanding of distributed systems concepts, including concurrency, fault tolerance, and performance tradeoffs
Ability to design and build well-defined, stable APIs and service interfaces for consumption by other teams and systems
Ability to build ETL pipelines that efficiently process large amounts of data using tools like PySpark.
Proficiency with cloud and container platforms, including containerized workloads and orchestration systems (e.g., AWS or GCP, Docker, Kubernetes)
Ability to collaborate effectively across teams, working closely with other engineers while maintaining independent execution
Ownership mindset, able to take responsibility for a work area and deliver high-quality, reliable systems
Ability to mentor and coach other engineers, promoting best practices and raising the engineering bar
Architectural thinking skills, with the ability to contribute meaningful ideas and practical solutions in design and architecture discussions
Ability to communicate complex technical concepts clearly to both technical and non-technical stakeholders

Responsibilities

Design, build, and operate core services and infrastructure for AI development, evaluation, and deployment
Own a significant surface area end to end, including production health and on-call
Turn open-ended problems into scoped, sequenced work — for yourself and for others
Raise the bar through design and code review, and mentor engineers on the team
Partner with product, research, and infrastructure teams on tradeoffs and roadmap
Work closely with machine learning engineers to write and maintain production-grade backend services that power AI-driven capabilities and agent applications
Design and implement APIs and service integrations that enable AI capabilities to be consumed across Workday products and platforms
Build and operate data ingestion and ETL pipelines that support AI application workflows
Apply distributed systems principles in production to address scalability, concurrency, fault tolerance, and performance challenges
Ensure systems meet enterprise requirements for security, privacy, robustness, and compliance
Own services through their full lifecycle, including deployment, monitoring, debugging, and ongoing operational improvements