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    Senior Machine Learning Engineer - Boston, United States - Evolution Recruitment Solutions, USA

    Evolution Recruitment Solutions, USA
    Evolution Recruitment Solutions, USA Boston, United States

    1 week ago

    Default job background
    Healthcare
    Description

    HealthTech Space: Sr ML Engineer/Sr Data Scientist

    Boston, MA (5 days onsite)

    Evolution is partnering with a well funded start up in the Boston area, who are looking for a Senior ML Engineer. If you're looking to join a company where you have the opportunity to grow with a company. They're using the latest technology to improve the lives of the elderly, frail or immobile individuals. You'll be joining a small team where your voice will be heard. They're looking for a Sr ML Engineer who has a strong theoretical foundation to develop the mix of standard and novel algorithms to help power their advanced platform.

    Day to day:

    • Contributing to the architecture and design of the data science software ecosystem that runs on the embedded sensor platform.
    • Designing, training, and testing machine learning solutions from concept to functional prototypes that are ready for implementation.
    • Working with our product team to identify opportunities for extracting actionable health data from our sensor suite.
    • Collaborate with a diverse team of engineers, scientists, clinicians and business development professionals and work in a dynamic and fast-paced environment
    • Work within our ISO13485 medical device development processes.

    Qualifications and skills:

    • MS or PhD in Artificial Intelligence, Machine Learning, Statistics, or a related field or equivalent on-the-job experience.
    • Experience working with sensor data from robotics or other real-world systems, including video, audio, LIDAR, radar, etc.
    • Fluency in Python, machine learning frameworks (Pytorch, Tensorflow, etc), data science tools and cloud services preferred
    • Extensive, practical knowledge of Machine Learning techniques for images, deep learning network architectures (CNNs, GANs, etc.), regularization, loss functions, optimization strategies, etc.
    • stack or with a deep learning framework such as TensorFlow/Keras or Pytorch
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