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    Research Engineer - San Francisco, United States - Eon Systems PBC

    Eon Systems PBC
    Eon Systems PBC San Francisco, United States

    4 weeks ago

    Default job background
    Description
    Eon collects large-scale neuroscientific data sets to train machine learning based brain emulations.

    We believe it is possible to scale this technology in a safe, secure and trustworthy manner in the next decade and empower humanity in unprecedented ways.

    Role


    Collaborating with a diverse team, including product managers, researchers, and engineering departments, your role involves conducting research on the application of cutting-edge of ML technologies to large-scale neuro datasets and transforming these insights into scalable, production-ready solutions.

    Responsibilities

    Design, train, and fine-tune transformer-based ML models and systems, ensuring their applicability and effectiveness in neuroscience.

    Develop and maintain production-grade ML systems, ensuring their scalability, efficiency, and reliability.

    Implement benchmarks that evaluate quality, safety, security, and trustworthiness in ML models and systems developed.

    Work in tandem with cross-functional teams, including product development and data infrastructure

    Engage in collaborative research efforts to explore new ML architectures, including image and video transformer models and multimodal systems.

    Contribute to the creation of state-of-the-art (SOTA) foundation models for both invasive and non-invasive neuroscientific datasets.

    Skills


    Demonstrated exceptional ability (3-5+ years) in ML engineering, particularly with PyTorch, including hands-on experience with training and fine-tuning transformer-based machine learning models.

    Demonstrated capability in developing production-level machine learning systems.

    Any of the following
    Experience with image and video transformer models.

    Expertise in training multimodal models and experimenting with novel architectures.

    Experience with applying machine learning techniques to neuroscientific datasets

    Previous work on scaling laws for modalities

    We expect everybody, independent of their role to be
    Practicing proactive, concise, and clear written communication.

    Exceptionally output driven and a well-calibrated, fast, autonomous, and diligent problem-solver.

    Excited about startup athmosphere - high initiative, agile, and a can-do attitude in a fast changing environment.

    Representative projects

    These are examples of projects that you would be working on when joining us:
    Using gpt architectures to train a non-invasive brain activity foundation model based on public datasets

    Implement a modality agnostic ML training pipeline for neuroscientific datasets to train multimodal brain data models

    Create synthetic data sets based on ML models that helps to align various datasets or improve overall performance of models

    Salary

    Competitive salaries, including equity, apply.

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