Who you are

• Highly self-motivated, always looking to take on work and get great pleasure from delivering production scale machine learning solutions to customers
• A collaborative teammate who uses positive leadership to coordinate and deliver results across teams
• A fast learner, detail oriented, decisive, and enjoys a fast-paced work environment
• Flexible, reliable, outgoing and has a positive work attitude
• 3+ years understanding of Python in both production and ETL settings
• 2+ years of building Data or MLOps pipelines using Python, Airflow, Databricks, or other similar cloud native services
• 2+ years experience on AWS, Vertex AI, and Kubernetes
• 2+ years experience in operationalization of Data Science projects using at least one of the popular frameworks or platforms (e.g. Airflow, Kubeflow, AWS Sagemaker, Google AI Platform) - Kubeflow in particular is a plus
• 1+ years experience building both data / ETL pipelines as well as model training infrastructure, and working with GPUs
• 1+ years Experience managing and supporting Docker, Kubernetes, Spark, CI/CD, Git-Ops
• 1+ years Experience with data versioning, ML model management, lifecycle, and reproducibility
• Experience with ML frameworks such as PyTorch, Keras, Transformers, SKLearn
• Experience with fine-tuning NLP models and with HuggingFace
• Experience with AWS services especially EKS
• 1+ years with MLOps tools like TFX, MLFlow, Kubeflow, Apache Spark, etc
• B.S. in a relevant field - (E.g. Computer Science, Mathematics, Engineering). M.S. or Ph.D are nice, but not required
• If you have a willingness to explore the unknown, be a technological pioneer and have passion around improving customer experiences then we want to hear from you!

What the job involves

• Workday is looking for a hardworking Machine Learning Operations engineer to contribute to the strategic mission of reaching workers where they are at
• You will join an energetic, open minded and supportive group of engineers to redefine how workers engage with Enterprise applications
• Implement MLOps tools, frameworks, and platforms to support ML development, deployment, and governance
• Create and maintain a repeatable and reusable ML workflow for model training, evaluation, deployment, and maintenance
• Improve tracking and monitoring of models, experiments, artifacts, and data
• Engage with data engineers and data scientists in feature engineering efforts
• Diagnose and resolve ML workflow and production issues quickly
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Benefits

• 401k plan
• Company equity
• Vision and dental insurance
• Work from home opportunities
• Health insurance
• On-site health clinic (Pleasanton office)
• Virtual primary care
• Flexible time-off policy
• Global mental health resources
• Global dedicated on-site clinical counselors
• Global well-being days
• $25k fertility/family planning benefits
• North Star financial wellness
• Retirement funds and matching