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 features and capabilities that can be scaled out to hundreds of use cases within Workday. Illuminate: The next generation of Workday AI is unlocking a whole new level of productivity and human potential by accelerating manual tasks, assisting every employee, and ultimately transforming entire business processes. With more than 70 million users under contract generating more than 800 billion transactions a year on our platform, Illuminate leverages the world's largest and cleanest HR and Finance dataset. The combination of this data-with Illuminate's ability to understand the context behind it-enables Workday to unlock value in a way no competitor can. Join us as we change the way millions of people work.About the Role
We are developing ML-powered Information Retrieval and Recommendation services and platforms to modernize and simplify user interactions with Workday. As a machine learning engineer, you will help develop tailored user experiences using advanced LLMs, Knowledge Graphs, personalization, and predictive analysis. You will collaborate with other engineers to deliver ML solutions across Workday's product ecosystem and utilize software and data engineering stacks to enable training, deployment, and lifecycle management of various ML models. Additionally, you will develop and deploy new APIs/microservices using docker/kubernetes at scale and leverage Workday's vast computing resources on rich datasets to deliver transformative value to our customers. Sound like your kind of challenge?
About You
In addition to contributing to feature and service development, you must have an approach of continuous improvement, passion for quality, scale, and security. You must be curious and prepared to question or challenge choices and practices where they don't make sense to you or could be improved. You also should have a product approach and strong intuition around how ML can drive a better customer experience. Lastly, a strong sense of ownership and teamwork are essential to succeed in this role.
Key Responsibilities:
- Own exploration, design and execution of advanced ML models, algorithms and frameworks that deliver value to our users
- Apply machine learning techniques including LLMs, knowledge graphs, deep learning including generative models, natural language understanding, topic modeling, GNNs and named entity recognition to analyze large sets of HR and Finance-related text data, and design and launch pioneering cloud-based machine learning architectures
- Own the performance, scalability, metric based deployed evaluation, and ongoing data driven enhancements of your products
- Keep abreast of the latest advancements in NLP research, techniques, and tools and apply this knowledge to ML Features. Serve as a technical role model for more junior engineers
- Respond to alerts and debug production issues as part of on-call rotation
Basic Qualifications- Senior MLE
- Bachelor's (Master's or PhD preferred) degree in engineering, computer science, physics, math or equivalent
- 5+ yrs experience as a member of a data science or machine learning science, machine learning engineering, or other relevant software development team building applied machine learning products, including taking products through applied research, design, implementation, production, and production-based evaluation
- 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
- 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
- Experience with data engineering and data wrangling using e.g. Pandas and PySpark and other industry tools used to build scalable machine learning systems, such as Kubernetes and Docker
Basic Qualifications- MLE
- Bachelor's (Master's or PhD preferred) degree in engineering, computer science, physics, math or equivalent
- 3+ yrs experience as a member of a data science or machine learning science, machine learning engineering, or other relevant software development team building applied machine learning products, including taking products through applied research, design, implementation, production, and production-based evaluation
- 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
- 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
- Experience with data engineering and data wrangling using e.g. Pandas and PySpark and other industry tools used to build scalable machine learning systems, such as Kubernetes and Docker
Other Qualifications:
- Familiarity with LLMs such as Gemini, Llama, GPT models and their applications in real-world scenarios
- Exposure to advanced techniques such as reinforcement 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. Ability to teach, mentor and lead through influence
- Bonus points for relevant PhD and/or machine learning related research publications
Posting End Date: 3/17/2025
If hired in Colorado, click here for information about Workday's comprehensive benefits in Colorado: https://workdaybenefits.com/us/welcome-to-workday-benefits/prospective-workmates.
The application deadline for this role is the same as the posting end date stated.
Workday Pay Transparency Statement
The annualized base salary ranges for the primary location and any additional locations are listed below. Workday pay ranges vary based on work location. As a part of the total compensation package, this role may be eligible for the Workday Bonus Plan or a role-specific commission/bonus, as well as annual refresh stock grants. Recruiters can share more detail during the hiring process. Each candidate's compensation offer will be based on multiple factors including, but not limited to, geography, experience, skills, job duties, and business need, among other things. For more information regarding Workday's comprehensive benefits, please click here.
Primary Location: USA.CO.Boulder
The application deadline for this role is the same as the posting end date stated as below:
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.
Pursuant to applicable Fair Chance law, Workday will consider for employment qualified applicants with arrest and conviction records.
Workday is an Equal Opportunity Employer including individuals with disabilities and protected veterans.
Are you being referred to one of our roles? If so, ask your connection at Workday about our Employee Referral process!
