Research, architect, and develop ML systems for member coaching distributed between edge hardware and the cloud

Collaborate with machine learning and edge ML engineers to translate prototypes into production

Partner with product and user experience teams to ensure consistent user experience in bandwidth-constrained environments and to align with member impact and health insights goals

Contribute to architectural decisions and mentor team members in ML best practices

Bachelor's degree in Computer Science, Electrical/Computer Engineering, Applied Mathematics, or a related field; Master’s or PhD degree preferred

5+ years of experience as a Machine Learning Scientist or similar role with a focus on applied research, preferably related to voice and/or text-based conversational systems 

Experience training, fine-tuning, and deploying state-of-the-art deep learning architectures to production 

Experience with time-series foundation models and self-supervised training approaches

Experience pre-training and fine-tuning small language models and/or building natural language understanding (NLU) models than run on resource-constrained targets

Experience with cloud platforms (AWS or GCP) and familiarity with modern MLOps practices such as CI/CD, model versioning, monitoring, and observability

Strong communication and collaboration skills across cross-functional teams

Strong commitment to embracing and leveraging AI tools in day-to-day tasks, ensuring AI-assisted work aligns with the same high-quality standards as personal contributions