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    Machine Learning Engineer - San Mateo, CA, United States - Kuraray America, Inc.

    Kuraray America, Inc.
    Kuraray America, Inc. San Mateo, CA, United States

    3 weeks ago

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    Description

    The Role:

    We are seeking a self-motivated, experienced Machine Learning Engineer* to develop, deploy, and refine tools to accelerate our antibody design cycle.

    *At BigHat we believe in titles that are commensurate with skill set, relative organizational impact, and value contribution; more experienced candidates are encouraged to apply, with the understanding that responsibilities and title would adjust as appropriate.

    At BigHat Biosciences, our machine learning stack is tightly integrated with a high-throughput wet lab to rapidly design, validate, and iteratively refine the first generation of ML-engineered antibody therapeutics.

    Our bespoke platform channels data from automated lab instruments to data processing pipelines, custom validation and QC, ML data ingestion ETLs, and finally, through predictive ML models and sequence optimization algorithms which produce the next set of antibody designs, ideally one step closer to a beneficial therapeutic but always generating valuable training data.

    You're not interested in any model thrown at any data.

    Motivated by an enthusiasm for the possibility of addressing unmet patient need, and a curiosity about the underlying biology, as an ML Engineer you'll apply your world-class ML skillset to refine and expand this state-of-the-art protein engineering platform, improving the effectiveness with which it can be used to design new therapeutics.

    Job Responsibilities

    Rapidly implement, evaluate and deploy predictive machine learning models of diverse antibody biophysical properties, for dataset scales from hundreds to millions, to support BigHat's therapeutic portfolio.

    Design and develop production-grade infrastructure for better model training, tracking, benchmarking and lab validation, across diverse tasks in a continuous data generation setting.

    Accelerate our multiobjective antibody optimization programs by developing, identifying or refining data- and domain-appropriate ML-driven protein sequence design tools.

    Work closely with ML and DS team members to identify inefficiencies or potential improvements and coordinate or implement solutions.

    Provide support for ongoing ML-guided antibody engineering therapeutics programs at BigHat.

    Collaborate closely with BigHat engineering, data science, and lab teams to ensure ML tooling is tailored to our experimental platform and therapeutic development goals.

    Preferred Qualifications

    BS/MS 5/3+ years of hands-on experience crafting, refining and deploying ML models in the wild.
    Strong competency in Python, familiarity with pytorch, experience deploying ML and DS stacks on AWS.
    Experience with relational databases, RESTful APIs.
    Excellent communication skills, sufficient biomedical domain knowledge to interact effectively with diverse scientific teams.
    Enjoys a fast paced environment and executing across multiple projects.
    Nice-to-haves include experience with protein structure modeling and biophysics, NGS data, and front-end (React).
    About BigHat Biosciences
    BigHat Biosciences designs safer, more effective biologic therapies for patients using machine learning and synthetic biology.

    BigHat integrates a wet lab for high-speed characterization with machine learning technologies to guide the search for better antibodies.

    We apply these design capabilities to develop new generations of safer and more effective treatments for patients suffering from today's most challenging diseases.


    BigHat is a Series B biotech outside San Francisco with a team-oriented, inclusive, and family-friendly culture.

    Our broad pipeline of wholly-owned and partnered therapeutic programs span many disparate indications with high unmet need, such as cancer, inflammation, and infectious disease.

    BigHat has raised
    $100M from top investors, including Section 32, a16z, and 8VC.
    #J-18808-Ljbffr
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