Machine Learning Engineer (Financials)

As a ML Engineer on the FIN Hyperautomation AI team, you will develop intelligent user experiences powered by advanced tools like (but not limited to) generative AI.

You will work with other engineers to deliver ML solutions across Workday’s ML infrastructure, from training ML models to developing APIs, as well as managing the lifecycle of such products and services.

You will be backed by Workday’s vast computing resources and rich datasets, to deliver transformative value to our customers

Own exploration, design, implementation, and deployment of features

The evaluation, scalability, and observability of these features

Apply ML techniques, including LLMs and natural language understanding, to intelligently process business documents and, for example, automate critical customer workflows

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

5+ years 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

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

5+ years of professional experience with cloud computing platforms (e.g. AWS, GCP, etc.)

5+ years of professional experience with Python and supporting numeric libraries, with experience in shipping production code and models

Strong understanding of statistical analysis, ML paradigms and algorithms, and natural language processing, with a focus for information retrieval and/or recommendation system use cases

3+ years of professional experience in developing with LLMs, RAG, and AI agents

5+ years of professional experience building scalable, production-level ML services on platforms such as Kubernetes

5+ years of professional experience in ML and deep learning frameworks & toolkits, such as scikit-learn, Pandas, PySpark, Pytorch, TensorFlow

5+ years of professional experience in building information retrieval systems

Professional experience in independently solving ambiguous, open-ended problems and technically leading a team