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New Grad Machine Learning Engineer

CardinalHire

New Grad Machine Learning Engineer

Austin, TX +1 location
Paid
  • Responsibilities

    Company Description
    Our startup is an AI research company that is focused on the hard problems that enterprises have. Today, most North American organizations haven’t managed to deploy AI in production yet. The key reasons behind this include incomplete and noisy datasets, the exorbitant cost of finding, hiring and retaining esoteric talent required to put an AI/ML system in production, and the black-box nature of neural-net based AI/ML models that sometimes result in predictions that can’t be explained easily and may introduce bias. We are working on a number of research areas to address these issues. Our research will be packaged into easy-to-use pay-as-you-go cloud service(s) that will be accessible to all enterprises later this year. In the meantime, we are partnering with a few select organizations who want early access to our research, to apply it to the problems they currently have. You can always apply for an invitation to get early access.

    Required Skills: Python, Machine Learning, Deep Learning System, Artificial Intelligence

    Job Description
    We are looking for an Entry Level Machine Learning Software Engineer to join our AI startup and help build the product that applies unsupervised learning to model the world and automatically create and manage production-grade AI systems. Ideal candidates have a strong AI background and are familiar with building higher-level abstractions for various common AI/ML techniques. As an initial member of the founding team, you get to own a significant chunk of the company, shape its culture and work on state-of-the-art science and technology.

    General Requirements

    • BS or MS or PhD in Computer Science
    • Knowledgable in deep reinforcement learning and deep neural networks
    • Acute understanding of automated feature extraction and dataset augmentation
    • Experienced at building Machine Learning and Artificial Intelligence models

    Responsibilities

    • Implement various algorithms to do automated feature extraction and dataset augmentation
    • Optimize runtimes of neural network algorithms
    • Automate various parts of the AI development workflow
    • Come up with new techniques in unsupervised learning, dataset augmentation and deep reinforcement learning

    #ZR

  • Locations
    San Francisco, CA • Austin, TX