Principal Machine Learning Engineer (Evisort AI)

About the role

Join Workday as a Principal Machine Learning Engineer, where you will 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 leverage Workday’s vast computing resources on rich datasets. A strong sense of ownership and teamwork is essential for success in this role. Key missions: 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.. Develop and deploy new products at scale and leverage Workday’s vast computing resources on rich datasets to deliver transformative value to our customers.. Lead, mentor, and manage ML Engineering teams, taking ownership of development lifecycle and sprint planning; fostering a culture of collaboration, transparency, innovation, and continuous improvement. Profile: - 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 - 4+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow - Stay up to date with advancements in AI, LLMs, RAG, autonomous agents and orchestration frameworks to drive innovation - Professional experience in independently solving ambiguous, open-ended problems and technically leading teams - Other Qualifications: - 6+ years of proven experience with cloud computing platforms (e.g. AWS, GCP, etc.) - 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 - 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 - Deep understanding of statistical analysis, unsupervised and supervised machine learning algorithms, and natural language processing for information retrieval and/or recommendation system use cases - 6+ years of professional experience in building services to host machine learning models in production at scale - Excellent interpersonal and communication skills, with the ability to build strong relationships across teams and stakeholders