Principal AI Engineer - Evisort

Workday Workday · Enterprise · Seattle, WA +3

Principal AI Engineer to develop and deploy AI-first products for Document Intelligence, CLM, and Contract Intelligence, leveraging LLMs, Knowledge Graphs, and predictive analysis. The role involves building ML solutions at scale across Workday's product ecosystem, with a focus on user experience, quality, and continuous improvement. Requires extensive experience in applied ML product development, production deployment, and LLM/GNN model usage.

What you'd actually do

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

Skills

Required

  • 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
  • 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

Nice to have

  • Stay up to date with advancements in AI, LLMs, RAG, autonomous agents and orchestration frameworks to drive innovation
  • Excellent interpersonal and communication skills, with the ability to build strong relationships across teams and stakeholders

What the JD emphasized

  • building applied machine learning products at scale
  • taking products through applied research, design, implementation, production, and production-based evaluation
  • building services to host machine learning models in production at scale
  • working with large language models (LLMs), text generation models, and/or graph neural network models for real-world use cases
  • 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

Other signals

  • AI platform for managing people, money, and agents
  • Document Intelligence AI
  • Workday's CLM and Contract Intelligence offerings
  • build AI first products
  • develop tailored user experiences using advanced LLMs, Knowledge Graphs, personalization, and predictive analysis
  • deliver ML solutions across Workday’s product ecosystem
  • develop and deploy new products at scale
  • leverage Workday’s vast computing resources on rich datasets to deliver transformative value to our customers
  • continuous improvement, passion for quality, scale, and security
  • product approach and strong intuition around how ML can drive a better customer experience
  • strong sense of ownership and teamwork
  • building applied machine learning products at scale
  • taking products through applied research, design, implementation, production, and production-based evaluation
  • building services to host machine learning models in production at scale
  • working with large language models (LLMs), text generation models, and/or graph neural network models for real-world use cases
  • 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
  • technically leading teams