UX Researcher Job at US012 Marsh & McLennan Agency LLC in Fremont

We are on a mission to ensure every baby lives the healthiest life possible by reimagining care with artificial intelligence (AI). We’re starting off with the Total Parenteral Nutrition (TPN) prescription process. By replacing error-prone, manual TPN workflows with AI-driven precision, we’re enhancing safety, streamlining the process, while also reducing costs.

Our works are featured in Science Translational Medicine and Nature Medicine. We are funded by both the National Institute of Child Health and Human Development (NICHD) and Silicon Valley venture capital firms.

We’re looking for an individual passionate and curious about how to leverage advances in AI to improve clinical practice. You’ll work directly with NICU teams to ensure our solution solves real-world problems, while also collaborating with an agile startup team to shape the features of our products.

Responsibilities:

• Work with NICU teams to implement AI solutions that address clinical challenges.
• Collaborate with startup team members to develop product features.

Minimum Qualifications:

• Ph.D. in a quantitative field with research experience in building/applying machine learning models during PhD/industry or postdoctoral experience in Machine Learning.
• Excellent publication and external funding track record.
• Interest (but not necessarily expertise) in medicine and biology.
• Familiarity with modern AI/ML platforms and libraries such as PyTorch, TensorFlow, and Jax.
• Familiarity with GenAI and/or in RAG for developing retrieval modules.

Preferred Qualifications:

• History of publications in leading AI/ML/Bioinformatics conferences and journals.
• Diverse experience in varied AI/ML concepts.
• Track record in development of open-source software adopted by the research community.
• Experience in medicine and/or biology.

Keywords: Machine Learning, Large Language Model, Foundation Model, Data Science, Deep Learning, Multitask Learning, Transfer Learning, Causal Inference, Causal Structure, Reinforcement Learning, Clustering, Classification, Visualization, R, Python, Julia, Bioinformatics, Graph Neural Network, Multiomics, Human Activity Monitoring, Actigraphy, Pregnancy, Precision Medicine, Personalized Medicine, EHR, Electronic Health Records, Wearable Devices.