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Roles and Responsibilities:
Developing and implementing novel machine learning algorithms particularly in the area of LLM to provide automation of clinical tasks using one or more of medical images, electronic medical records, waveforms, and clinical reports.
Demonstrating algorithms to meet accuracy requirements on general subject population through statistical analyses and error estimation.
Exploring learning from human feedback and assisting humans evaluating AI.
Building prototypes to enable development of high-performance AI algorithms in scalable, product-ready code.
Initiating/proposing unique and promising deep learning capabilities, developing new and innovative algorithms and technologies, pursuing patents where appropriate.
Working with large-scale datasets, designing, and developing generative algorithms.
Staying current on published state-of-the-art algorithms and competing technologies.
:
Master’s Degree in a “STEM” major (Science, Technology, Engineering, Mathematics) or equivalent field plus 5 years AI development for industrial applications in a commercial settingORPh.D. in a “STEM” major (Science, Technology, Engineering, Mathematics) or equivalent field plus 3 years AI development for industrial applications in a commercial settingORPh.D. in a “STEM” major (Science, Technology, Engineering, Mathematics) or equivalent field plus 1 years of AI development for Healthcare applications.
Publications as first author on LLM/Foundational/Multimodal models or self-supervised learning (SSL).
Demonstrated expertise in building large scale AI such as generative AI models, large vision/language models, and multi-modal AI models for problems related to segmentation, detection, quantification, measurements, classification, etc.
Implementation experience with a variety of high-level languages (e.g. Python, C++)
Experience with high-dimensional imaging data and waveform/time-series data.
Desired Characteristics:
Experience and demonstrated capability to handle challenges with vague or abstract problem definition.
Experience with frameworks and tools such as DeepSpeed, HuggingFace, Megatron, PyTorch lightning, etc.
Experience with various MLOps, ModelOps, FMOps (Foundation Model Ops) methods.
Experience working with large scale AI training.
An in-depth understanding of machine learning algorithms and modeling (e.g., semi-supervised or weakly supervised learning, generative models, transfer learning, optimization, large language models, etc.)
Track record in developing machine learning solutions using massive real-world data for solving real world business problems.
In depth experience with Spark/Hadoop and either PyTorch/Tensorflow
Experience creating production environment data analytics and applications
Inclusion and Diversity
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