Your work days are brighter here.

At Workday, it all began with a conversation over breakfast. When our founders met at a sunny California diner, they came up with an idea to revolutionize the enterprise software market. And when we began to rise, one thing that really set us apart was our culture. A culture which was driven by our value of putting our people first. And ever since, the happiness, development, and contribution of every Workmate is central to who we are. Our Workmates believe a healthy employee-centric, collaborative culture is the essential mix of ingredients for success in business. That’s why we look after our people, communities and the planet while still being profitable. Feel encouraged to shine, however that manifests: you don’t need to hide who you are. You can feel the energy and the passion, it's what makes us unique. Inspired to make a brighter work day for all and transform with us to the next stage of our growth journey? Bring your brightest version of you and have a brighter work day here.

About the Team

About the Role

As a machine learning engineer passionate about ML Ops, you will

• Implement MLOps tools, frameworks, and platforms to support ML development, deployment, and governance

• Create 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

We will challenge you to apply your best creative thinking, analysis, problem-solving, and technical abilities to make an impact on thousands of enterprises and millions of people.

Basic Qualifications

• 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,

• 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

Other Qualifications

• 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

About You

• 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.

Workday Pay Transparency Statement 

The annualized base salary ranges for the primary location and any additional locations are listed below.  Workday pay ranges vary based on work location. As a part of the total compensation package, this role may be eligible for the Workday Bonus Plan or a role-specific commission/bonus, as well as annual refresh stock grants. Recruiters can share more detail during the hiring process. Each candidate’s compensation offer will be based on multiple factors including, but not limited to, geography, experience, skills, job duties, and business need, among other things. For more information regarding Workday’s comprehensive benefits, please click here.

Our Approach to Flexible Work
 

With Flex Work, we’re combining the best of both worlds: in-person time and remote. Our approach enables our teams to deepen connections, maintain a strong community, and do their best work. We know that flexibility can take shape in many ways, so rather than a number of required days in-office each week, we simply spend at least half (50%) of our time each quarter in the office or in the field with our customers, prospects, and partners (depending on role). This means you'll have the freedom to create a flexible schedule that caters to your business, team, and personal needs, while being intentional to make the most of time spent together. Those in our remote "home office" roles also have the opportunity to come together in our offices for important moments that matter.

Pursuant to applicable Fair Chance law, Workday will consider for employment qualified applicants with arrest and conviction records.

Workday is an Equal Opportunity Employer including individuals with disabilities and protected veterans.

Are you being referred to one of our roles? If so, ask your connection at Workday about our Employee Referral process!