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    Machine Learning Engineer - Cambridge, Cambridgeshire, United Kingdom - Flagship Pioneering

    Flagship Pioneering
    Flagship Pioneering Cambridge, Cambridgeshire, United Kingdom

    3 weeks ago

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    Description
    Flagship Labs 97 Inc. (FL97) is a privately held, early-stage company pioneering the application of artificial intelligence to transform all science.

    Our Physical Sciences effort is developing a novel AI and data-driven approach to materials discovery and development to accelerate the transition to a sustainable economy.

    FL97 was founded by Flagship Pioneering, an innovation enterprise that conceives, creates, resources, and builds companies that invent breakthrough technologies to transform health care, agriculture, and sustainability.

    FL97 is seeking a dedicated and skilled Machine Learning Engineer, Nucleic Acids to join our team. This role will focus on MLOps for training in large scale ML models of RNA and/or DNA.

    You will be part of a dynamic, cross-functional team responsible for developing and deploying machine learning models for sequence design.

    Working closely with biologists, bioinformaticians, software developers, and automation engineers, you will contribute to the development of ML models for a range of therapeutic applications.

    The ideal candidate has a strong background in machine learning, as well as either experience in biotech industry or a record of scientific achievement, with a focus on MLOps, model training, and deployment.

    Developing, training, and deploying machine learning models for RNA/DNA sequence design.
    Implementing MLOps practices to streamline the model development and deployment process.
    Collaborating with cross-functional teams to integrate ML models into the data pipelines for our labs.
    Implementing rigorous testing, documentation, and model benchmarking.
    Master's degree in computer science, computational biology, physics, or other quantitative disciplines
    Experience with MLOps practices and tools including version control, automated testing, and CI/CD
    Experience in at least one ML framework (PyTorch/TensorFlow/Jax) and robust experience in the Python data science ecosystem.
    Experience with large language models (e.g. autoregressive LLMs) for biological sequences is a plus.
    Familiarity with bioinformatics tools and databases for pre-processing and analyzing biological sequence data.

    Since its launch in 2000, Flagship has originated and fostered more than 100 scientific ventures, resulting in more than $90 billion in aggregate value.

    Many of the companies Flagship has founded have addressed humanity's most urgent challenges:

    vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture.

    Flagship has been recognized twice on FORTUNE's "Change the World" list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies, and has been twice named to Fast Company's annual list of the World's Most Innovative Companies.

    Flagship Pioneering and our ecosystem companies are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

    We are dedicated to building diverse and inclusive teams and look forward to learning more about your unique background.

    Flagship Pioneering and its affiliated Flagship Lab companies (collectively, "FSP") do not accept unsolicited resumes from any source other than candidates.

    Will you now or in the future require sponsorship for employment visa status (e.g., H-1B visa status)?

    If you'll require the company to commence ("sponsor") an immigration or work permit case in order to employ you, either now or at some point in the future, then you should select Yes.

    Are you able to work in the specified job location, or are you willing to relocate for this position?
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