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    Machine learning engineer - San Francisco, CA, United States - Scale AI, Inc.

    Scale AI, Inc.
    Scale AI, Inc. San Francisco, CA, United States

    2 weeks ago

    Default job background
    Description

    Scale's Foundational ML team conducts research on new foundational capabilities, with the goal of innovating models and algorithms that unlock net-new capabilities for Scale's applied-ML teams and the broader AI community.

    Scale is uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle.

    You will work closely with Scale's Generative AI team focused on building models to accelerate and optimize AI adoption for some of the largest companies in the world.

    At Scale, our research is driven by product needs. Your focus will be on developing new foundational models, algorithms, and forms of supervision for Generative AI. You will lead writing, publishing, and adoption of your work internally with applied teams. You will be involved end-to-end from the inception and planning of new research agendas.

    You'll be creating high quality datasets, implementing models and associated training and evaluation stacks, producing high caliber publications in the form of peer-reviewed journal articles, blogs, white papers, and internal presentations & documentation.

    If you are excited about shaping the future AI via fundamental innovations, we would love to hear from you


    You will:
    Evaluate, adapt, and develop new state of the art language and/or multimodal foundation models
    Work with applied ML and product teams to identify opportunities for service improvement or new capabilities
    Explore approaches that integrate human feedback and assisted evaluation into existing product lines
    Work closely with internal customers to prototype, build, and integrate your models into production service


    Ideally you'd have:
    A track record of high-caliber publications in peer-reviewed machine learning venues (e.g. NeurIPS, ICLR, ICML, EMNLP, CVPR, AAAI etc.)
    At least 3 to 5 years of model training, deployment and maintenance experience in a production environment.
    Strong skills in NLP, LLMs and deep learning.
    Solid background in algorithms, data structures, and object-oriented programming.
    Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment.
    Strong high-level programming skills (e.g., Python), frameworks and tools such as Pytorch lightning, kuberflow, TensorFlow, transformers, etc.
    Strong written and verbal communication skills to operate in a cross functional team environment and to broadcast your work efficiently and with splash


    Nice to haves:
    Experience in dealing with large scale AI problems, ideally in the generative-AI field.

    Demonstrated research expertise in post-training methods &/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc.

    The base salary range for this full-time position in our hub locations of San Francisco, New York, or Seattle, is $240,000-$290,000.

    Compensation packages at Scale include base salary, equity, and benefits.

    The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training.

    Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Scale employees are also granted Stock Options that are awarded upon board of director approval.

    You'll also receive benefits including, but not limited to:
    Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.
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