About the Team

About the Role

As a Machine Learning Engineer on the AI Platform team, you will develop tailored user experiences using advanced Agentic AI, LLMs and RAG. You will collaborate with other engineers to deliver ML solutions across Workday’s product ecosystem and use current software and data engineering stacks to enable training, deployment, and lifecycle management of a variety of ML models; supervised and unsupervised. Additionally, you will develop and deploy new APIs/services using Docker/Kubernetes at scale and leverage Workday’s vast computing resources on rich datasets to deliver transformative value to our customers. Sound like your kind of challenge?

About You

You are a strong technical leader with deep Python expertise and solid machine learning engineering skills, capable of writing beautiful, well-designed code while delivering solutions efficiently. Specifically, you will:

• Own exploration, design and implementation of features for our sophisticated ML platforms, pipelines and services.

• Be responsible for evaluation, scalability and observability of these features.

• Apply machine learning techniques including LLMs and natural language understanding to analyze large sets of HR and Finance-related text data, and design and launch pioneering cloud-based machine learning architectures

• Stay up to date with advancements in AI, LLMs, RAG, autonomous agents and orchestration frameworks to drive innovation

Basic Qualifications:

• Bachelor’s (Master’s or PhD preferred) degree in engineering, data/computer science, physics, math or equivalent

• 3+ yrs full-time professional experience as a member of a data science, machine learning engineering, or other relevant software development team building machine learning products from the ground up at scale. This includes taking products through applied research, design, implementation, evaluation, and production.

• 3+ years of full-time hands-on professional experience in developing ETL pipelines and inference services that use large language models (LLMs) and text generation models in production. This includes the full machine learning life cycle - data processing, model fine-tuning, model deployment and model evaluation

• 3+ years of full-time professional experience with Python and supporting libraries in production

• 3+ years of full-time professional experience with data engineering and data wrangling using e.g. Pandas and PySpark and other industry tools used to build scalable machine learning systems, such as Kubernetes and Docker

• 3+ years of full-time professional experience with cloud computing platforms (e.g. AWS, GCP, etc.)

• 3+ years of being able to communicate clearly and effectively in a cross-functional setting with product managers, app teams, and leadership

Other Qualifications:

• 3+ years of full-time professional experience in building information retrieval systems.

• 3+ years of full-time professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow, and Sklearn

• Deep understanding of statistical analysis, unsupervised and supervised machine learning algorithms, and natural language processing for information retrieval and/or recommendation system use cases

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 .

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 2home office2 roles also have the opportunity to come together in our offices for important moments that matter.