Sr. Software Engineer-Agent Engineer
| Company | Workday |
|---|---|
| Location | Boulder, CO, USA |
| Salary | $153600 – $230400 |
| Type | Full-Time |
| Degrees | Bachelor’s, Master’s, PhD |
| Experience Level | Senior |
Requirements
- Bachelor’s degree in a relevant field, such as Computer Science, Mathematics, or Engineering.
- 5+ years experience working on a data science, machine learning or related software development team.
- 5+ years experience with Python and supporting numeric libraries, with experience in shipping production code and models.
- 3+ years of experience object oriented programming in Java
- 2+ years experience in machine learning and deep learning frameworks & toolkits such as Pytorch and Sklearn.
Responsibilities
- Build Agentic Workflows: Design and develop sophisticated AI agents that reason, learn, and interact across complex business processes to enhance productivity and decision-making.
- Integrate AI Frameworks: Embed secure, scalable, and reliable agentic capabilities into core Adaptive Planning features—driving smart automation for FP&A users.
- Partner Cross-Functionally: Collaborate with world-class engineers and product managers to bring ideas to life—from concept to customer impact.
- Own Projects End-to-End: Lead the full AI development lifecycle—problem framing, data prep, model training, deployment, evaluation, and continuous improvement.
- Leverage Rich Planning Data: Harness Workday Adaptive Planning’s vast enterprise datasets to tune and optimize your AI models for high-value outcomes.
Preferred Qualifications
- PhD or MS degree in a relevant field, such as Computer Science, Mathematics, or Engineering. Publications in machine learning research are highly desirable.
- Practical experience with generative models, large language models (LLM), retrieval augmented generation (RAG) systems, transformer neural networks.
- Experience with cloud computing platforms (e.g. AWS, GCP), containerization technologies (e.g. Docker) and data engineering pipelines (e.g. ETL).
- Experience developing and deploying machine learning solutions using large-scale datasets, including specification design, data collection and labeling, model development, validation, deployment, and ongoing monitoring.
- An ability to balance speed with delivering high-quality, practical solutions. Proven perseverance in overcoming meaningful problems.