Workdayposted 21 days ago
$179,200 - $268,800/Yr
Full-time • Senior

About the position

As a Principal Software Development Engineer for Machine Learning, you will be a pivotal technical contributor, working closely with machine learning engineers to architect, build, and deploy robust, scalable, and performant AI systems across Workday's product ecosystem. You will play a key role in transforming innovative ML research into production-ready solutions, primarily agentic AI capabilities encompassing planning, reasoning, and action execution frameworks.

Responsibilities

  • Drive Architectural Design and Implementation: Own the architectural design and implementation of sophisticated AI systems and agentic AI capabilities, with a focus on advanced patterns such as tool calling, supervisor agents, multi-agent architectures, and human-in-the-loop integration.
  • Ensure solutions are highly scalable, performant, and resilient in production environments, defining technical roadmaps and standard methodologies for ML system development and MLOps.
  • Build and Deploy Core ML Infrastructure: Architect, implement, and deploy secure, RESTful web services in Python and Kubernetes.
  • Design and build robust, multi-tenant runtime architectures enabling fast inference and scaling to millions of users, seamlessly integrating with existing Workday components and leveraging cloud deployment standard methodologies.
  • Develop and Optimize Data Pipelines: Lead the development and deployment of Python and Spark-based data pipelines for collecting, joining, transforming, and loading large-scale datasets essential for model training and inference.
  • Focus on efficiency, reliability, and data quality.
  • Provide technical leadership and guidance to fellow engineers, fostering a culture of engineering excellence, advocating for software development standard methodologies, and participating in design reviews and code quality initiatives.
  • Collaborate multi-functionally to translate requirements into technical designs, taking ownership of creative, high-quality solutions.
  • Apply and continuously advance industry-standard software engineering practices, including automation, observability, scalability, and MLOps, to deliver clean, maintainable, and testable code.

Requirements

  • Bachelor's degree in Computer Science, Engineering, or a related technical field.
  • 8 or more years of experience in production-level Software Development.
  • 4 or more years of experience in Python, with a consistent track record of shipping production code and systems.
  • 4 or more years of experience building scalable data pipelines and working with large-scale datasets.
  • 4 or more years of proven experience deploying production services to cloud platforms (e.g., AWS, Azure, GCP) and using containerization technologies (e.g., Docker, Kubernetes) for MLOps.

Nice-to-haves

  • MS/PhD degree in a relevant field, such as Computer Science or Engineering.
  • Deep technical expertise in the engineering, deployment, and MLOps of advanced machine learning solutions (e.g., generative models, LLMs, RAG, and AI agents).
  • Strong understanding of scalable distributed systems, performance optimization, database technologies (e.g., PostgreSQL, Redis), and robust API development.
  • Proven algorithmic thinking and a track record of designing, implementing, and analyzing efficient algorithms for complex problems.
  • Demonstrated ability to build flexible, reusable, and well-documented software components, with comprehensive experience in code testing strategies (unit, integration, end-to-end) in a continuous deployment environment.
  • Strong sense of ownership and a proven ability to deliver high-quality, finished products efficiently.
  • Excellent communication and collaboration skills, emphasizing team collaboration, knowledge-sharing, and delivering customer impact.

Benefits

  • Workday Bonus Plan or a role-specific commission/bonus.
  • Annual refresh stock grants.
  • Flexible work schedule with a combination of in-person and remote work.
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