Senior Machine Learning Engineer/Machine Learning Engineer III

Workday•Seattle, WA

1d•$163,000 - $288,000•Hybrid

About The Position

Workday is seeking a Senior Machine Learning Engineer/Machine Learning Engineer III to join their Agent Factory team. This role involves designing and building core ML systems for Workday's next generation of AI agents. You will work within a small, senior, cross-functional pod, owning the integration of models, agent logic, and orchestration layers in production. This includes the full lifecycle from problem framing and data strategy to deployment, monitoring, and continuous improvement. The role focuses on implementing and evolving frameworks for LLM-powered agents, such as RAG pipelines, workflow orchestration, evaluation, and feedback loops, ensuring scalability, observability, and enterprise readiness. This position is at the intersection of ML and platform engineering, requiring close collaboration with software engineers, product managers, and data scientists to integrate agents into the Workday stack. You will stay current with emerging techniques in agentic architectures and apply strong engineering judgment to create reliable, explainable systems operating at global scale.

Requirements

For P4, Senior Machine Learning Engineer: 7+ 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.
For P3, Machine Learning Engineer III: 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.
For P4: 3+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow.
For P3: 2+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow.
For P4: 4+ years of professional experience in building services to host machine learning models in production at scale.
For P3: 3+ years of professional experience in building services to host machine learning models in production at scale.
For P4: 2+ years of demonstrated experience working with large language models (LLMs), text generation models, and/or graph neural network models for real-world use cases.
For P3: 1+ years of demonstrated experience working with large language models (LLMs), text generation models, and/or graph neural network models for real-world use cases.
For P4: 4+ years of proven experience with cloud computing platforms (e.g. AWS, GCP, etc.).
For P3: 3+ years of proven experience with cloud computing platforms (e.g. AWS, GCP, etc.).
Bachelor’s (Master’s or PhD preferred) degree in engineering, computer science, physics, math or equivalent.
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.
Excellent interpersonal and communication skills, with the ability to build strong relationships across teams and stakeholders.

Nice To Haves

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

Responsibilities

Design and build the core ML systems behind Workday’s next generation of AI agents.
Own how models, agent logic, and orchestration layers come together in production across the full lifecycle.
Implement and evolve frameworks for LLM-powered agents, including RAG pipelines, workflow orchestration, evaluation, and feedback loops.
Ensure solutions are scalable, observable, and enterprise-ready.
Partner closely with software engineers, product managers, and data scientists to integrate agents deeply into the Workday stack.
Stay hands-on with emerging techniques in agentic architectures.
Apply strong engineering judgment to turn emerging techniques into systems that are reliable, explainable, and built to operate at global scale.
Lead, mentor, and/or manage ML Engineering teams, taking ownership of the development lifecycle and sprint planning.
Foster a culture of collaboration, transparency, innovation, and continuous improvement.