ML Engineer, LLMs & Retrievals on AI Platform

Workday, Inc.; Vancouver; Hybrid; CAD 128,000 - 192,000; Full time

Job summary

Workday, Inc. in Vancouver, BC is seeking a Machine Learning Engineer on the AI Platform Context and Retrievals team to develop tailored user experiences using advanced Agentic AI, LLMs and RAG, collaborating with engineers to deploy ML solutions across Workday's product ecosystem at scale.

You will write production-grade Python, build scalable ML pipelines, and contribute to cloud-based architectures using Docker and Kubernetes while staying ahead of AI advances and mentoring junior engineers.

Qualifications

• Bachelor's degree in engineering, data/computer science, physics, math or equivalent.
• 3+ yrs experience building ML products at scale including production deployment.
• 3+ yrs Python and numeric libraries experience with production code.
• 3+ yrs cloud platforms experience (AWS, GCP, etc.).
• Deep understanding of ML, NLP and information retrieval concepts.

Responsibilities

• Own exploration, design and implementation of features for ML platforms, pipelines and services.
• Be responsible for evaluation, scalability and observability of these features.
• Apply ML techniques including LLMs and NLP to analyze HR/Finance text data, design and launch cloud-based architectures.
• Stay up to date with AI advancements to drive innovation.
• Serve as a technical role model for junior engineers.

Skills

Python programming

ML engineering

Data science

Leadership

Problem solving

Education

Bachelor's degree in engineering, data/computer science, physics, math or equivalent

Master's or PhD preferred

Tools

Docker

Kubernetes

PySpark

TensorFlow

PyTorch

Scikit-learn

Pandas

AWS

GCP

Job description

Workday, Inc. in Vancouver, BC is seeking a Machine Learning Engineer on the AI Platform Context and Retrievals team to develop tailored user experiences using advanced Agentic AI, LLMs and RAG, collaborating with engineers to deploy ML solutions across Workday's product ecosystem at scale.

You will write production-grade Python, build scalable ML pipelines, and contribute to cloud-based architectures using Docker and Kubernetes while staying ahead of AI advances and mentoring junior engineers.