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    Staff ML Engineer - Seattle, United States - Grid

    Grid
    Grid Seattle, United States

    3 weeks ago

    Default job background
    Description
    About Us

    Today's financial system is built to favor those with money.

    Grid's mission is to level that playing field by building financial products that help users better manage their financial future.

    The Grid app lets users access cash, build credit, spend money, optimize their taxes, and lots, lots more.

    Grid is a fast-growing team that's deeply passionate about making a difference in the lives of millions. We're solving huge problems and believe that every team member has a big role to play. Come join our growing team in our brand new Seattle office

    The role


    We're adding a Staff Machine Learning (ML) Engineer to our team to help us build and scale our core product lines.

    You'll work closely with data, product, engineering and business leaders to implement and scale statistical inference systems. Partnering closely with data scientists, you'll architect and develop machine learning infrastructure for offline research and online serving.


    With an active userbase and numerous fascinating opportunities for data products, your work will ship quickly, have high visibility and will likely make significant impact on Grid's business performance.

    You'll also have the time and space to "do it right".

    Grid's long-term strategy is focused on data-driven products that require accuracy and precision at every layer, from model to deployment.


    Projects will include fraud detection, prevention and mitigation in novel arenas, such as our first-to-market Income Protection program; risk underwriting for various lending/advance programs; predictive analytics to drive our payout and repayments systems; and more.

    The team

    We're focused on serving our users and building a robust business above all else.

    To this end, Grid's team members experience high levels autonomy and ownership, and as a company we value curiosity, learning and growth.


    As our first full time ML Engineer, you'll have an opportunity not only to identify key leverage points for our machine learning implementations, but also to set the standard for Grid's statistical inference and machine learning practice.

    The Tech Stack

    Our backend tech stack is based on GCP, Go, protobufs, BigQuery and MySQL.

    We have built our platform from the ground up to optimize for clean data sources, and we have made numerous investments into data warehousing, streaming analytics infrastructure and offline data cleanliness.

    As a result, we think Grid is positioned for efficient and powerful machine learning in the near future.


    What you'll do:

    Architecture & Design:
    Research and select modern implementation strategies for machine learning, in order to ensure a smooth transition from research to production

    Model Development:
    Implement and deploy models that enable strategically relevant business objectives, such as enabling growth, mitigating fraud, controlling risk, etc

    Continuous Delivery:
    Enable rapid iteration in a complex, financially sensitive environment that demands high degree of accuracy, precision and control

    Infrastructure Management:
    Set up and operate ML research infrastructure as needed by data scientists across the company, including data lake management, ML ops, etc

    Production Engineering:
    Ship production-worthy software with all of the necessary details required for modern devops, including testing, observability and automation.

    Collaborative Engineering:
    Collaborate with data scientists, product engineers, business managers, and more, in order to ensure seamless integration of your data products and APIs with the overall tech stack

    Foster DS @ Grid:
    Help build out data science as a team and practice at Grid


    What we're looking for:

    Machine Learning Expertise:
    Proven experience in data science and machine learning, including a strong background in statistical inference, machine learning frameworks and techniques (Logistic Regression, Naive Bayes, Random Forest, etc) and computer science

    Technical Depth:
    Full in-depth comprehension of relevant tools, frameworks and architectures, such that your solutions are correct, holistic and scalable

    Pragmatic Efficiency:
    A knack for getting the right things done in the right order of operations

    Autonomy and Initiative:
    Ability to work independently and take ownership of projects, showcasing a proactive approach to identifying key leverage points for data products

    Curiosity and Optimism:
    People who are constantly asking why the world around them work the way it does, and who have the will to change it

    Self Starter:
    Confidence to prioritize work and delivery demonstrable results on a tight cadence

    Domain Knowledge:
    Demonstrated experience or understanding of the financial industry, especially in the context of building and scaling FinTech products

    #J-18808-Ljbffr
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