Machine Learning Engineer

3 Months ago • All levels • Business Development

About the job

Job Description

Machine Learning Engineer to optimize models, improve ad delivery efficiency, and work on cutting-edge projects. Requires strong ML background, Python, TensorFlow/PyTorch, model optimization, and collaboration skills.
Must have:
  • Machine Learning
  • Python, TensorFlow
  • Model Optimization
  • Team Collaboration
Good to have:
  • Ad Tech
  • C++ Development
  • Recommendation Systems
  • Published Papers
Perks:
  • Global Team
  • Innovative Projects
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About AppLovin

AppLovin makes technologies that help businesses of every size connect to their ideal customers. The company provides end-to-end software and AI solutions for businesses to reach, monetize and grow their global audiences. For more information about AppLovin, visit: www.applovin.com.

To deliver on this mission, our global team is composed of team members with life experiences, backgrounds, and perspectives that mirror our developers and customers around the world. At AppLovin, we are intentional about the team and culture we are building, seeking candidates who are outstanding in their own right and also demonstrate their support of others.

Fortune recognizes AppLovin as one of the Best Workplaces in the Bay Area, and the company has been a Certified Great Place to Work for the last four years (2021-2024). Check out the rest of our awards HERE.

  • About AppLovin AXON

    AXON驱动着全球最大的移动应用广告投放产品,也为AppLovin的新业务电子商务广告产品提供支持,还通过AXON Connect 为第三方客户提供推荐系统服务。 AXON由一小群才华横溢的完美主义者组成,我们以成为世界最好为目标。这个精简又充满活力的团队,在整个公司以创新精神和不懈追求卓越而被人们所知。我们不断突破可能的界限,确保AXON始终处于推荐技术的前沿,并为合作伙伴和用户提供无与伦比的性能和价值。 我们致力于打造一个全北京最有活力、创造力,也最有趣的团队。

    A Day In The Life

    我们正在寻找一位有才华且有激情的机器学习工程师加入我们的Research Science团队。该你将专注于广告技术中的机器学习模型优化和性能提升,旨在提高广告投放的效果和效率。 理想的候选人不仅要具备卓越的学历和技术背景,还要能够在团队合作中发挥积极作用,持续跟踪和应用前沿技术,自主发现并解决问题。 在AXON,你将与全世界最优秀的开发者一起,打造最佳的广告系统和基础设施。你可以把你的能力和经验直接转化为可以看见的业务增长。 在AXON,我们鼓励大胆创新,我们崇尚工程师文化,我们拒绝形式主义。如果你崇尚技术,希望用技术创造财富、改变世界,你将在这里找到志同道合的伙伴。

    The Impact You’ll Make

    • 开发和改进广告投放相关的机器学习模型,提升模型的预测准确性和效果。
    • 与跨职能团队合作,改进数据流水线,优化模型推理性能,完善监控系统。
    • 持续关注机器学习和广告技术领域的最新进展,评估其对公司业务的潜在影响。将最新的研究成果应用于实际项目中,推动技术创新。
    • 主动发现广告投放系统中的潜在问题和优化空间,提出可行的解决方案。并设计和执行实验,验证假设并优化模型和系统性能。
    • 与我们才华横溢的机器学习工程师、数据科学家和软件工程师密切合作,无缝集成你的解决方案到我们的平台。

    Who You Are

    • 拥有计算机、数学、统计学、机器学习等相关专业的硕士或博士学位。
    • 扎实的机器学习理论基础和实践经验,熟悉常用的机器学习算法和工具(如Python, TensorFlow, PyTorch等)。
    • 具有模型优化和推理性能优化的实际经验,了解相关技术和方法。
    • 优秀的团队合作能力,善于沟通和协作,能够在团队中积极贡献。
    • 强烈的学习意愿和自主发现解决问题的能力,能够快速适应技术变化。

    Additional Strengths

    • 具有广告技术或相关领域的工作经验。
    • 具有C++开发经验
    • 发表过推荐系统或广告顶会(如KDD、NeurIPS、SIGIR、Recsys等)论文者优先。
AppLovin is proud to be an equal opportunity employer that is committed to inclusion and diversity. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status, or other legally protected characteristics. Learn more about EEO rights as an applicant here.

If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send us a request at accommodations@applovin.com.

AppLovin will consider for employment all qualified applicants with criminal histories in a manner consistent with applicable law. If you’re applying for a position in California, learn more here.
Please read our Global Applicant Privacy Notice to learn more about how AppLovin processes your personal information.
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About The Company

AppLovin makes technologies that help businesses of every size connect to their ideal customers. The company provides end-to-end software and AI solutions for businesses to reach, monetize and grow their global audiences. AppLovin is headquartered in Palo Alto, California, with several offices globally.

Beijing, Beijing, China (On-Site)

Toronto, Ontario, Canada (On-Site)

Palo Alto, California, United States (On-Site)

Toronto, Ontario, Canada (On-Site)

Palo Alto, California, United States (On-Site)

Palo Alto, California, United States (On-Site)

Toronto, Ontario, Canada (On-Site)

Berlin, Berlin, Germany (On-Site)

Toronto, Ontario, Canada (On-Site)

Beijing, Beijing, China (On-Site)

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