Generative AI Engineer - Health
The Health Sensing team builds outstanding technologies to support our users in living their healthiest, the happiest lives by providing them with objective, accurate, and timely information about their health and well-being. As part of the larger Sensor Software & Prototyping team, we take a multimodal approach using a variety of sensors across hardware platforms, such as camera, wearable sensors, and natural language.
In this role, you will be at the forefront of developing, evaluating and improving generative models for real-world health/wellbeing applications. You will work across the ML development cycle to develop repeatable, scalable pipelines for model training, experimentation, and deployment, using innovative technologies such as distillation, knowledge injection, and reinforcement learning with human feedback.
- Expertise in ML development standard methodologies, with hands-on experience building efficient, repeatable, scalable pipelines for model training, experimentation, and analysis
- Proficiency using python and deep learning frameworks (e.g. PyTorch) in a peer-reviewed environment. Ability to write clean, performant code following standard software development practices.
- Experience collaborating with ML scientists to improve model performance via thorough experimentation and failure analysis. For example, experience with synthetic data generation, data collection design, sampling strategies, adversarial testing, or perturbation studies.
- Familiarity with post-training techniques for applying large language models, such as distillation, chain of thought prompting, and knowledge injection.
- Strong communication skills, comfort working with multiple engineering teams on complex projects, and experience contributing to an inclusive team culture
- Experience or strong interest in building consumer digital health and wellness products
- BS degree or equivalent experience
- MS in computer science, data science, statistics, or similar and a minimum of 3 years of relevant proven experience
- Knowledge of health informatics or experience with complex health data sources (e.g. electronic health records, medical ontologies, wearables)
- Experience deploying machine learning models in a production environment
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