Principal Machine Learning Engineer - Evisort AI

Your work days are brighter here.

At Workday, it all began with a conversation over breakfast. When our founders met at a sunny California diner, they came up with an idea to revolutionize the enterprise software market. And when we began to rise, one thing that really set us apart was our culture. A culture which was driven by our value of putting our people first. And ever since, the happiness, development, and contribution of every Workmate is central to who we are. Our Workmates believe a healthy employee-centric, collaborative culture is the essential mix of ingredients for success in business. That’s why we look after our people, communities and the planet while still being profitable. Feel encouraged to shine, however that manifests: you don’t need to hide who you are. You can feel the energy and the passion, it's what makes us unique. Inspired to make a brighter work day for all and transform with us to the next stage of our growth journey? Bring your brightest version of you and have a brighter work day here.

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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, and 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 incredible customer base of 70M+ users.

About the Role

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 also should have a product approach and strong intuition around how ML can drive a better customer experience. Lastly, a strong sense of ownership and teamwork are essential to succeed in this role. 

About You

Basic Qualifications

• 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, TensorFlow

• 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

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