Principal Machine Learning Engineer - Evisort AI

About the Team

Join the Evisort AI team at Workday, which powers Document Intelligence AI and Workday’s CLM and Contract Intelligence offerings. Our mission is to change the way business deals get done. We build ground‑breaking AI technology that can read and understand contract language to make every part of the deal‑making process—from drafting, negotiating, reviewing, approving, or managing the contracts—happen faster, better, with reduced risks.

We build AI‑first products, automate manual work, freeing up our customers’ time and accelerating their businesses. You will be joining the Evisort AI team, which functions as a startup within Workday. This is your opportunity to build at the pace of innovation of a startup while backed by the enormous support and impacting Workday’s customer base of 70M+ users.

As a Principal Machine Learning Engineer, you will help develop tailored user experiences using advanced LLMs, Knowledge Graphs, personalization, and predictive analysis. You will collaborate with other engineers to deliver ML solutions across Workday’s product ecosystem and utilize software and data engineering stacks to enable training, deployment, and lifecycle management of various ML models. You will develop and deploy new products at scale and leverage Workday’s vast computing resources on rich datasets to deliver transformative value to our customers.

In addition to contributing to feature and service development, you must have an approach of continuous improvement, passion for quality, scale, and security. You must be curious and prepared to question or challenge choices and practices where they don’t make sense to you or could be improved. You should have a product approach and strong intuition around how ML can drive a better customer experience.

A strong sense of ownership and teamwork are essential to succeed in this role.

• 10+ 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.
• 4+ years of professional experience in machine learning and deep learning frameworks & toolkits such as PyTorch, Tensor Flow.
• 6+ years of professional experience in building services to host machine learning models in production at scale.
• 3+ years of demonstrated experience working with large language models (LLMs), text generation models, and/or graph neural network models for real‑world use cases.
• 6+ years of proven experience with cloud computing platforms (e.g., AWS, GCP, etc.).
• Proven track record of successfully leading, mentoring, and/or managing ML engineering teams, taking ownership of development lifecycle and sprint planning; fostering a culture of collaboration, transparency, innovation, and continuous improvement.
• Bachelor’s (Master’s or PhD preferred) degree in engineering, computer science, physics, math or equivalent.

• 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.
• 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.

The annualized base salary ranges for the primary location and any additional locations are listed below. Workday pay ranges vary based on work location. As part of the total compensation package, this role may be eligible for the Workday Bonus Plan or a role‑specific commission/bonus, as well as annual refresh stock grants. Recruiters can share more detail during the hiring process.

Each candidate’s compensation offer will be based on multiple factors including, but not limited to, geography, experience, skills, job…