Machine Learning Engineer

W2 Candidates Only

Location: USA

Visa: Open to any visa type with valid work authorization in the USA

Experience Required: 6 to 12 years

Level: Mid to Lead positions

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. You will develop and deploy new products at scale and leverage Workday's vast computing resources on rich datasets to deliver transformative value to our customers.



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.





Basic Qualifications

• 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, including taking products through applied research, design, implementation, production, and production-based evaluation
• 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, Google Cloud Platform, 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

Other Qualifications:

• Stay up to date with advancements in AI, LLMs, RAG, autonomous agents and orchestration frameworks to drive innovation
• Deep understanding of statistical analysis, unsupervised and supervised machine learning algorithms, and natural language processing for information retrieval and/or recommendation system use cases
• Professional experience in independently solving ambiguous, open-ended problems and technically leading teams
• Excellent interpersonal and communication skills, with the ability to build strong relationships across teams and stakeholders