1 day ago
Base Salary
$163k - $288k/yr
Responsibilities
- Own the exploration, design, and implementation of features for ML platforms, pipelines, and services.
- Evaluate, scale, and improve the observability of ML platform features.
- Apply LLMs, natural language understanding, and other machine learning techniques to HR- and finance-related text data.
- Design and launch cloud-based machine learning architectures and production model-hosting services.
- Develop and deploy APIs and services using Docker and Kubernetes at scale.
- Enable training, deployment, and lifecycle management for supervised and unsupervised ML models.
- Stay current with AI, LLM, RAG, autonomous-agent, and orchestration-framework advancements.
- Serve as a technical role model for junior engineers and technically lead teams.
Requirements
- Bachelor’s degree in engineering, data science, computer science, physics, mathematics, or an equivalent qualification; a master’s degree or PhD is preferred.
- At least 6 years of experience on a data science, machine learning engineering, or relevant software development team building applied machine learning products at scale.
- At least 5 years of professional Python experience with supporting numeric libraries, including shipping production code and models.
- At least 5 years of professional experience with cloud computing platforms such as AWS or GCP.
- At least 3 years of experience building information retrieval systems or graph-based recommendation systems.
- At least 3 years of hands-on professional experience developing production LLMs, text-generation models, or graph-based machine learning models, including data processing, fine-tuning, deployment, and evaluation.
- At least 3 years of experience building scalable production services that host machine learning models.
- At least 3 years of experience with machine learning and deep learning frameworks and toolkits such as PySpark, PyTorch, TensorFlow, and Scikit-learn.
- At least 3 years of professional experience with data engineering and data wrangling using tools such as Pandas and PySpark, plus scalable ML-system tools such as Kubernetes and Docker.
- Deep understanding of statistical analysis, supervised and unsupervised machine learning, and natural language processing for information retrieval or recommendation systems.
- Experience independently solving ambiguous problems, technically leading teams, and communicating effectively with product managers, application teams, and leadership.
Benefits
- Flexible work arrangement combining remote and in-person work, with at least 50% of each quarter spent in the office or in the field.
- Role may be eligible for the Workday Bonus Plan or a role-specific commission/bonus and annual refresh stock grants.
- The application deadline is 09/30/2026.
- Base pay ranges vary by location; listed ranges include $171,600-$257,400 for Boulder and $163,000-$288,000 for additional U.S. locations.
Tech Stack
Categories
About Workday
Workday builds cloud-based enterprise applications for human capital management and financial management, including payroll, time tracking, expenses, planning, and procurement, sold on a subscription basis with professional services. Founded in 2005 and headquartered in Pleasanton, California, it is a public company traded on NASDAQ as WDAY. Organizations worldwide use Workday to unify HR and finance data, automate processes, and apply AI to workforce and financial operations.