Develops and scales ML systems for AI coaching on edge hardware and cloud, focusing on time-series models, small language models, and NLU for health insights. Requires 5+ years experience, deep learning expertise, and collaboration across teams.
Salary not listed
On-site5+ YOEML Engineering
About the role
Responsibilities
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
Qualifications
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 that 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
Skills
Deep LearningTime-Series ModelsSelf-Supervised LearningSmall Language ModelsNatural Language UnderstandingNluAWSGCPMLOpsCI/CDModel Versioning
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