engineering
Posted May 4Senior Software Development Engineer - AI Core
at Workday
Vancouver, CanadaRemote
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Requirements
- 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.
- About the Team Do you want to architect the intelligence layer that powers enterprise AI? The AI Core team, part of Workday's AI Platform organization, sits at the frontier of agentic AI — building the foundational systems that enable autonomous agents to reason, act, and deliver deep value across Workday's HR and Finance products.
- The AI Core team tackles challenging problems at the intersection of machine learning and enterprise-scale systems for information retrieval and recommendation use cases.
- About the Role As a Senior Software Development Engineer on the AI Core team, you will be primarily responsible for designing, building, and operating the software systems that host, run, and scale AI-powered agentic applications at Workday.
- you will: Design and implement production-grade services, APIs, and ETL pipelines in the ML RAG platform, which is consumed by numerous AI-driven Workday products and high-priority agents Apply distributed systems principles in production to address scalability, concurrency, fault tolerance, and performance challenges Automate CI/CD and testing workflows , and proactively look for ways to improve developer
- experience with Python development Bachelor’s degree in Computer Science, Engineering, or related discipline, or equivalent practical experience Other
- Qualifications: Technical Skills: Understanding of object-oriented design principles.
- Proficiency with advanced Python concepts such as asynchronous and concurrent programming, generators, higher-order abstractions, Pydantic and Pyspark. Preferred prior
- experience with building scalable services and pipelines for ML use cases in Production 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 Proficiency with cloud and container platforms, including containerized workloads and orchestration systems (e.g., AWS or GCP,