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    Engineering Manager, Risk - San Jose, United States - OKX

    OKX
    OKX San Jose, United States

    3 weeks ago

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    Description
    Who We Are

    OKX is revolutionising world systems through our cutting-edge digital asset exchange, Web3 portal and blockchain ecosystems. We are deeply committed to shaping a fairer, more transparent and accessible society through blockchain technology and to date, we have 50+ million users, 3000+ employees and 180+ countries believing in the same vision as us. We are safe and reliable, backed by our Proof of Reserves. As strong supporters of the Arts and Sports, we are proud partners of @McLarenF1 @ManCity @Tribeca.
    About the Opportunity

    We are the Risk Engineering team within OKX US Engineering based in San Jose. We measure, detect, prevent and enforce payment risks. The team has been growing very rapidly over the past year and is still growing. We are looking for a motivated engineering manager to help lead the OKBrain Infra and ML team. We are still at the middle stage of building pipelines and developing ML models, which creates great opportunities for this role to make impacts. If you are results driven, enjoy a fast paced environment, excited about catching and stopping payment fraud and scam, welcome joining us. It will be a very joyful journey growing together with the rapid OKX growth.
    What You'll Be Doing
    • Responsible for building up and managing the OKBrain Infra and ML team at OKX
    • Align with product and org objectives, and coordinate with XFN teams on delivering key results
    • Define long term technical roadmap for the team that is aligned with Risk Engineering org
    • Responsible for system design and technical solutions of data pipelines and ML models
      • Design and develop the risk data flows to collect and store data, tools to monitor and troubleshoot issues
      • Design and develop the feature store, provide feature data to consumers
      • Establish efficient labeling process, quality measurement process, and tooling support
      • Build the infrastructure to facilitate ML development, testing, and release
      • Develop ML models to measure payment risks, driven by key metrics defined by PM/DS/Eng
      • Build sampling process to iteratively improve the ML models performance
    What We Look For In You
    • 8+ years of industry experience including 3 - 5 years of people management experience leading a team of
    • At least 3 years experience in ML development. Proficient in developing pipeline and training models.
    • Comfortable with 50% management and 50% hands on with Python for ML or Java for Infra.
    • Comfortable with system design, coding, and code review, especially during code release time.
    • Results driven and data driven.
    Nice to Haves
    • Background in the payment risk domain on a global scale
    • Bilingual in Mandarin
    Perks & Benefits
    • Competitive total compensation package
    • L&D programs and Education subsidy for employees' growth and development
    • Various team building programs and company events
    • Wellness and meal allowances
    • Comprehensive healthcare schemes for employees and dependents
    OKX Statement

    The salary range for this position is $235,000 to $330,000. The salary offered depends on a variety of factors, including job-related knowledge, skills, experience, and market location. In addition to the salary, a performance bonus and long-term incentives may be provided as part of the compensation package, as well as a full range of medical, financial, and/or other benefits, dependent on the position offered. Applicants should apply via OKX internal or external careers site.

    OKX is committed to equal employment opportunities regardless of race, color, genetic information, creed, religion, sex, sexual orientation, gender identity, lawful alien status, national origin, age, marital status, and non-job related physical or mental disability, or protected veteran status. Pursuant to the San Francisco Fair Chance Ordinance, we will consider employment-qualified applicants with arrest and conviction records.
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