Machine Learning Engineer Intern contributing to AI solutions for financial services. Engaging in hands - on ML projects and real production issues in a hybrid working environment.
About the role
- Senior Machine Learning Engineer designing and building core ML systems for Workday’s AI agents. Collaborating with cross-functional teams on deployment, monitoring, and improvement of AI solutions.
Responsibilities
- Design and build the core ML systems behind Workday’s next generation of AI agents
- Own how models, agent logic, and orchestration layers come together in production—across the full lifecycle from problem framing and data strategy to deployment, monitoring, and continuous improvement
- Implement and evolve frameworks for LLM-powered agents, including RAG pipelines, workflow orchestration, evaluation, and feedback loops, ensuring solutions are scalable, observable, and enterprise-ready
- Partner closely with software engineers, product managers, and data scientists to integrate agents deeply into the Workday stack
- Stay hands-on with emerging techniques in agentic architectures while applying strong engineering judgment to turn them into systems that are reliable, explainable, and built to operate at global scale
Requirements
- 10+ years experience as a member of a data science, machine learning engineering, or other relevant software development team building applied machine learning products at scale
- 4+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow
- 6+ years of professional experience in building services to host machine learning models in production at scale
- 3+ years of demonstrated experience working with large language models (LLMs), text generation models, and/or graph neural network models for real-world use cases
- 6+ years of proven experience with cloud computing platforms (e.g. AWS, GCP, etc.)
- Proven track record of successfully leading, mentoring, and/or managing ML Engineering teams, taking ownership of development lifecycle and sprint planning; fostering a culture of collaboration, transparency, innovation, and continuous improvement.
- Bachelor’s (Master’s or PhD preferred) degree in engineering, computer science, physics, math or equivalent
Benefits
- Workday Bonus Plan or role-specific commission/bonus
- Annual refresh stock grants
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