Principal, Machine Learning Engineer

$192,000 - $288,000/Yr

Workday - Atlanta, GA

Full-time - Mid Level

Atlanta, GA

Publishing Industries

About the position

As a Machine Learning Engineer at Workday, you will play a crucial role in developing ML-powered search and generative AI services that enhance user interactions with Workday's products. This position involves building tailored experiences using advanced Large Language Models (LLMs) and predictive analysis, contributing to the modernization of Workday's applications. You will collaborate with other engineers to deliver scalable ML solutions, utilizing Workday's extensive computing resources to transform decision-making for global enterprises.

Responsibilities

• Own exploration, design and execution of advanced ML models, algorithms and frameworks that deliver value to our users.
• Preprocess and clean large amounts of unstructured text data to ensure quality and consistency for Natural Language Processing (NLP) and other ML model training.
• Engineer relevant features from textual data to facilitate accurate model predictions and classification.
• Apply machine learning techniques including LLMs, deep learning, natural language understanding, sentiment analysis, topic modeling, and named entity recognition to analyze large sets of HR-related text data.
• Design and launch pioneering cloud-based machine learning architectures.
• Train, validate, and fine-tune machine learning models using large-scale datasets to achieve robust performance.
• Own the performance, scalability, metric-based deployed evaluation, and ongoing data-driven enhancements of your products.
• Collaborate across teams to deliver your products through Workday end user applications.
• Keep abreast of the latest advancements in NLP research, techniques, and tools.

Requirements

• Bachelor's (Master's preferred) degree in engineering, computer science, physics, math or equivalent.
• 6 or more years of experience developing, deploying, and supporting high-performance systems in production.
• 6 or more years of experience with industry tools used to build scalable machine learning systems, such as AWS, SQL, Elasticsearch/Open Search, Kubernetes, Docker and/or Spark.
• 6 or more years of experience delivering applied machine learning products, including taking a product through design, implementation, and to production.
• 6 or more years of developing Machine Learning driven features with Python (including NumPy, SciPy, Pandas), JVM, and Linux.
• Proven theoretical and practical understanding of statistical analysis and machine learning algorithms, natural language processing, especially for supervised, unsupervised and self-supervised methods.
• Experience with generative models, large language models, and transformer based deep neural networks.

Nice-to-haves

• Experience with data engineering and data wrangling using e.g. Pandas and PySpark.
• Familiarity with LLMs such as Llama, different GPT models, and their applications in real-world scenarios.
• Exposure to advanced techniques such as reinforcement learning, imitation learning, and graph neural networks.
• Experience with cloud computing platforms (e.g. AWS, GCP) and containerization technologies (e.g. Docker).
• Bonus points for relevant PhD and/or machine learning related research publications.

Benefits

• Workday Bonus Plan or role-specific commission/bonus
• Annual refresh stock grants
• Flexible work schedule
• Comprehensive benefits package