Machine Learning Engineer III

Workday•Boulder, CO

4d•$136,200 - $240,000•Hybrid

About The Position

The AI Core 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 Core 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. You are a strong technical leader with deep Python expertise and solid software 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. Serve as a technical role model for more junior engineers.

Requirements

Bachelor’s (Master’s or PhD preferred) degree in engineering, data/computer science, physics, math or equivalent
3+ yrs experience as a member of a data science, machine learning engineering, or other relevant software development team building applied machine learning products at scale, including taking products through applied research, design, implementation, production, and production-based evaluation
3+ years of professional experience with Python and supporting numeric libraries, with experience in shipping production code and models
3+ years of professional experience with cloud computing platforms (e.g. AWS, GCP, etc.)

Nice To Haves

3+ years of professional experience in building information retrieval systems and/or graph-based recommendation systems.
3+ years of hands-on professional experience in developing large language models (LLMs), text generation models, or graph-based machine learning models for production, including data processing, model fine-tuning, model deployment and model evaluation
3+ years of professional experience building services to host machine learning models in production at scale
3+ years of professional experience in machine learning and deep learning frameworks & toolkits such as PySpark, Pytorch, TensorFlow, and Sklearn
3+ years of 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
Deep understanding of statistical analysis, unsupervised and supervised machine learning algorithms, and natural language processing for information retrieval and/or recommendation system use cases
Professional experience in independently solving ambiguous, open-ended problems and technically leading teams

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.
Serve as a technical role model for more junior engineers.