Machine Learning Engineer - Evisort

Workday Workday · Enterprise · Vancouver, BC +1

Machine Learning Engineer role focused on developing and deploying AI-first products for Document Intelligence and Contract Intelligence, leveraging LLMs, Knowledge Graphs, and predictive analysis. The role involves building ML solutions at scale within Workday's product ecosystem, with a focus on continuous improvement, product intuition, and customer experience.

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

  • 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
  • 2+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow
  • 2+ years of professional experience in building services to host machine learning models in production at scale
  • 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
  • 2+ 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
  • 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
  • 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.

Nice to have

  • Master’s or PhD preferred

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
  • demonstrated experience working with large language models (LLMs)
  • proven experience with cloud computing platforms (e.g. AWS, GCP, etc.)
  • Stay up to date with advancements in AI, LLMs, RAG, autonomous agents and orchestration frameworks
  • Deep understanding of statistical analysis, unsupervised and supervised machine learning algorithms, and natural language processing for information retrieval and/or recommendation system use cases
  • Proven track record of successfully leading, mentoring, and/or managing ML Engineering teams, taking ownership of development lifecycle and sprint planning

Other signals

  • building AI first products
  • automate manual work
  • 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