Senior Machine Learning Engineer
4 days ago
Apply remote type Flex locations Ireland, Dublin time type Full Time posted on Posted 2 Days Ago job requisition id JR-0094440
Your work days are brighter here.
At Workday, it all began with a conversation over breakfast. When our founders met at a sunny California diner, they came up with an idea to revolutionize the enterprise software market. Our culture, driven by our value of putting our people first, is central to who we are. Our Workmates believe a healthy employee-centric, collaborative culture is the essential mix of ingredients for success in business. We look after our people, communities, and the planet while still being profitable. Feel encouraged to shine, however that manifests: you don't need to hide who you are.
About the Team
We're working on making machine learning core to Workday's products by building data products and auto-ml models that can be scaled out to hundreds of use cases. Our work supports thousands of the largest global companies and more than 30 million end users. You will solve complex problems and influence machine learning and application development across Workday. Our team is passionate about teaching and learning, so be eager to share your expertise and learn from us.
About the Role
We're building an ML platform designed to power Search, Generative AI, and other services in order to modernize how users interact with Workday. As a platform machine learning engineer, you will help build an ML development platform designed to support advanced Large Language Models (LLMs), personalization, and predictive analysis. You will work closely with other data engineers, ML engineers, and software developers to deliver ML solutions that enable ML powered search and user experience across Workday's product ecosystem.
Key Responsibilities- Own exploration, design and execution of platform technology that enables advanced ML models, algorithms and frameworks that deliver value to our users.
- Build data pipeline tools to preprocess and clean large amounts of unstructured text data to ensure quality and consistency for Natural Language Processing (NLP) and other ML model training.
- Platformize feature engineering from textual data to facilitate accurate model predictions and classification.
- Apply machine learning techniques including LLMs, deep learning including generative models, natural language understanding, sentiment analysis, topic modeling, and named entity recognition.
- Enable training, validation, and fine-tune of 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.
- Bachelor's (Master's preferred) degree in engineering, computer science, physics, math or equivalent.
- 3 or more years of experience developing, deploying, and supporting high-performance systems in production.
- 3 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.
- 3 or more years of experience delivering applied machine learning products.
- 3 or more years of developing Machine Learning driven features with Python, JVM, and Linux.
- Proven theoretical and practical understanding of statistical analysis and machine learning algorithms.
- Experience with generative models, large language models, and transformer based deep neural networks.
- 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).
- Strong communication skills, with experience working across functions and teams.
- Resilience to obstacles, and ability to solve problems independently.
Our Approach to Flexible Work
With Flex Work, we're combining the best of both worlds: in-person time and remote. Our approach enables our teams to deepen connections, maintain a strong community, and do their best work.
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