Senior Machine Learning Engineer

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. And when we began to rise, one thing that really set us apart was our culture. A culture which was driven by our value of putting our people first. And ever since, the happiness, development, and contribution of every Workmate 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. That’s why 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. You can feel the energy and the passion, it's what makes us unique. Inspired to make a brighter work day for all and transform with us to the next stage of our growth journey? Bring your brightest version of you and have a brighter work day here.

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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 within Workday. As part of a global, high-growth technology company, 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. Join us as we change the way millions of people work.

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 - adding ease, intelligence and efficiency to everyday interactions. Platformization of all of these capabilities enables the rapid development of ML applications across a wide range of use cases at 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. You will apply current 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, exclusive 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 users. Sound like your kind of challenge?

About You

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 to analyze large sets of HR-related text data, and design and launch pioneering cloud based machine learning architectures.

• 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.

• 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.

Basic Qualifications

• 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, including taking a product through design, implementation, and to production

• 3 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.

Other Qualifications

• 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).

• Standout colleague, 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. We know that flexibility can take shape in many ways, so rather than a number of required days in-office each week, we simply spend at least half (50%) of our time each quarter in the office or in the field with our customers, prospects, and partners (depending on role). This means you'll have the freedom to create a flexible schedule that caters to your business, team, and personal needs, while being intentional to make the most of time spent together. Those in our remote "home office" roles also have the opportunity to come together in our offices for important moments that matter.

Are you being referred to one of our roles? If so, ask your connection at Workday about our Employee Referral process!