Senior Machine Learning Engineer/Machine Learning Engineer III

Workday•Seattle, WA

2d•$163,000 - $288,000•Hybrid

About The Position

Workday is seeking a Machine Learning Engineer to join the Agent Factory team, where they will design and build the core ML systems for Workday’s next generation of AI agents. This role involves working within a small, senior, cross-functional pod to own the end-to-end lifecycle of models, agent logic, and orchestration layers in production. The engineer will implement and evolve frameworks for LLM-powered agents, including 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. The role demands staying current with emerging techniques in agentic architectures and applying strong engineering judgment to create reliable, explainable systems that operate at global scale.

Requirements

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 P4)
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 P3)
3+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow (for P4)
2+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow (for P3)
4+ years of professional experience in building services to host machine learning models in production at scale (for P4)
3+ years of professional experience in building services to host machine learning models in production at scale (for P3)
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 P4)
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 P3)
4+ years of proven experience with cloud computing platforms (e.g. AWS, GCP, etc.) (for P4)
3+ years of proven experience with cloud computing platforms (e.g. AWS, GCP, etc.) (for P3)
Bachelor’s (Master’s or PhD preferred) degree in engineering, computer science, physics, math or equivalent
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
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

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 from problem framing and data strategy to deployment, monitoring, and continuous improvement.
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.
Take ownership of the development lifecycle and sprint planning.
Foster a culture of collaboration, transparency, innovation, and continuous improvement.