Generative AI Engineer - Health Sensing
The Health Sensing team builds outstanding technologies to support our users in living their healthiest, happiest lives by providing them with objective, accurate, and timely information about their health and well-being. As part of the larger Sensor SW & Prototyping team, we take a multimodal approach using a variety of sensors across HW platforms, such as camera, PPG, and natural languages.
In this role, you will be at the forefront of developing, evaluating and improving generative models for real-world health/wellbeing applications on their objective quality and alignment with human intent and perception, such as truthfulness, adaptability, and model generalizability. You will work on data and evaluation pipeline of both human and synthetic data for model evaluation, leverage ML technologies such as reinforcement learning with human feedback and adversarial models.
- Work across the entire ML development cycle, such as developing and managing data from various endpoints, managing ML training jobs with large datasets, and building efficient and scalable model evaluation pipelines
- Analyze model behavior, identify weaknesses, and drive design decisions with failure analysis. Examples include, but not limited to: model experimentation, adversarial testing, creating insight/interpretability tools to understand and predict failure modes, designing and improving human evaluation/annotation methods to accurately measure model performance
- Collaborate with engineers to build reliable end-to-end pipelines for long-term projects
- Work cross-functionally to apply algorithms to real-world applications with designers, clinical experts, and engineering teams across Hardware and Software
- Ability to independently run and analyze ML experiments for real improvements
- Bachelors, Masters or PhD in Computer Science, Data Science, or a related field; or equivalent experience
- Proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow)
- Ability to write clean, performant code and collaborate using standard software development practices
- Experience with applied LLM development, prompt engineering, chain of thought, etc.
- Experience in building data and inference pipelines to process large scale datasets
- Expertise in deep learning (e.g. hands-on training and evaluation experience with transformers)
- MS and a minimum of 3 years relevant proven experience or PhD in relevant fields
- Knowledge in human evaluation techniques, such as RLHF, and analyzing and deriving insights from that data
- Customer-focused / product oriented. Experience or strong personal interest in building consumer digital health and wellness products
- Strong communicator, comfortable working with multiple engineering teams on complex projects
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