Machine Learning Engineer

Found in: Talent IE C2 - 2 weeks ago

Dublin, Ireland Workday Limited Full time

About the Role

As a machine learning engineer, you will help develop tailored experiences for every user powered by advanced machine learning, Large Language Models (LLMs), personalization, and predictive analysis. You will work closely with other ML engineers and software developers to deliver ML solutions that enable personalized user experience across Workday’s product ecosystem. You will use modern software and data engineering stacks to enable training, deployment, and lifecycle management of a variety of ML models; supervised and unsupervised, deep learning and classical. You will develop new APIs/microservices and deploy them using docker/kubernetes at scale. You will use Workday’s vast computing resources on rich, best-in-class datasets to deliver value that transforms the way our customers make decisions and run their business. We will challenge you to apply your best creative thinking, analysis, problem-solving, and technical abilities to make an impact on thousands of enterprises and millions of people. Sound like your kind of challenge?

In this role, you would:

Be a member of the Human Capital Management (HCM) Skills Machine Learning team.

Own exploration, design and execution of advanced machine learning models, algorithms and frameworks that deliver value to our users.

Preprocess and clean large volumes 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 large language models, deep learning including generative models, natural language understanding, sentiment analysis, topic modeling, and named entity recognition to analyze large volumes of HR-related text data, and 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.

Be responsible for 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.

Be given autonomy and ownership over your work, but with the support of the entire organization.

Keep abreast of the latest advancements in NLP research, techniques, and tools.

Have extraordinary opportunities for career growth and learning in a fast-growing, forward-looking company.

About You

Basic qualifications:

3+ yrs experience as part of a data science or machine learning science, machine learning engineering, or other relevant software development team.

Proficiency in Python and supporting numeric libraries, with experience in shipping production code and models.

Experience in machine learning and deep learning frameworks & toolkits such as Pytorch, Tensorflow, and Sklearn. 

Good 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.

Strong experience building applied machine learning products, including taking a product through applied research, design, implementation, production, and production based evaluation.

Execution of projects for products handling large-scale, complex data sets, data modeling, and productizing machine learning algorithms.

Resilience to obstacles, and ability to solve problems independently.

M.S. in a relevant field, e.g. Computer Science, Mathematics, Engineering. 

Other Qualifications:

Experience with data engineering and data wrangling using e.g. Pandas and PySpark.

Familiarity with large language models 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).

Great teammate, strong communication skills, with experience working across functions and teams.

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