Machine Learning Engineer

Workday•Pleasanton, CA

12d•Hybrid

About The Position

The AI Foundations team, part of Workday’s AI Platform organization, tackles challenging problems at the intersection of machine learning, agentic reasoning, and enterprise-scale systems. Our work delivers critical AI platform capabilities and differentiated, deep-value agent applications. 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.

Requirements

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

Nice To Haves

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

Responsibilities

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