Your work days are brighter here.
About The Team
About The Role
About You
Key Responsibilities
- Build, deploy, tune, and optimize Python and Spark-based ETL pipelines for collecting, joining, transforming, and loading data used for model training and inference
- Build, deploy, monitor, and maintain secure, RESTful web services in Python and Kubernetes that power our agents and recommendations
- Design and implement multi-tenant runtime architectures that enable fast inference, scale to millions of users, and integrate with existing Workday components
- Understand and address complex system design challenges related to microservices, including caching, sharding, observability, and event-based architecture
- Collaborate with other platform teams to improve shared infrastructure and components
- Triage and address alerts and production issues as part of an on-call rotation
Basic Qualifications
- Bachelor's (Master's or PhD preferred) degree in engineering, computer science, or equivalent
- Strong proficiency in Python
- 5+ years of experience with production software development
- 2+ years of professional experience building scalable systems and optimizing performance of large-scale web services
- 2+ years of professional experience with data engineering and data wrangling using industry-standard tools like Pandas, PySpark, and Sagemaker to build scalable data pipelines
- 2+ years of professional experience scaling services with containerization technologies like Kubernetes and Docker
- 2+ years of professional experience with cloud computing platforms (e.g. AWS, GCP, etc.)
- Professional experience in independently solving ambiguous, open-ended problems
Other Qualifications
- Takes ownership and delivers complete products, balancing craftsmanship with the need to ship to customers.
- Standout colleague, strong communication skills, with experience working across functions and teams
- Ability to teach, mentor, and lead through influence (Not required for SDE )
- Professional experience in building information retrieval systems and/or recommendation systems.
- Professional experience in machine learning and deep learning frameworks and toolkits such as Pytorch, TensorFlow, and Sklearn
- Professional experience with Python and supporting numeric libraries, with experience in shipping production code and models
- Professional experience working with large language models (LLMs), text generation models, and/or graph neural network models for real-world use cases
Workday Pay Transparency Statement
Our Approach to Flexible Work spend at least half (50%) of our time each quarter in the office or in the field
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