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Machine Learning Engineer, Ads Measurement

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Location
Canada
Reddit

Job Description

Reddit is a community of communities. It’s built on shared interests, passion, and trust and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 82M+ daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit redditinc.com.

The Ads Measurement Modeling team is dedicated to enhancing the efficiency and user-friendliness of our advertising platform. We specialize in privacy enhancing technologies, identity resolution, and modeled conversions. We are looking for a Machine Learning Engineer to use privacy enhancing technologies to improve our capabilities. As a diverse group of software engineers, product managers, and ads experts, we are excited for you to join our team!

Responsibilities:

  • Design and develop applied machine learning models for modeled conversions and identity matching from ideation to production deployment.
  • Identify opportunities to enhance ad measurement capabilities by diving deep into our platform and understanding the needs of our advertisers.
  • Collaborate with backend software engineers
  • Conduct A/B tests and experiments to iterate and fine-tune algorithms and models.
  • Stay updated on state-of-the-art algorithmic techniques and recognize promising innovations, adapting them to Reddit's unique platform and community.
  • Design, implement, and maintain reliable, high-performance distributed systems.

Minimum Qualifications:

  • 2+ years of experience with applied machine learning models and large-scale ML systems. Includes experience, training, evaluating, testing, and deploying machine learning models
  • Proficiency with programming languages (Python, SQL) and statistical analysis.
  • Effective communication skills to collaborate with cross-functional teams and convey technical concepts to non-technical stakeholders.

Preferred Qualifications:

  • Experience of orchestrating complicated data pipelines and system engineering on large-scale dataset
  • Prior experience with Modeled Conversions, Ads Privacy, or Ads Measurement is a big plus
  • Prior experience with Sequence Modeling, Reinforcement Learning, and Transformer Architecture is a big plus

Benefits:

  • Comprehensive Health Benefits
  • Retirement Savings plan with matching contributions
  • Workspace benefits for your home office
  • Personal & Professional development funds
  • Family Planning Support
  • Flexible Vacation & Reddit Global Days Off

LI-Remote

Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve. Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, please contact us at ApplicationAssistance@Reddit.com.

Advice from our career coach

As a Machine Learning Engineer at Reddit, you will be part of the Ads Measurement Modeling team, focusing on privacy enhancing technologies, identity resolution, and modeled conversions. To stand out as an applicant for this role, here are some key tips:

  • Highlight your experience with applied machine learning models and large-scale ML systems in your resume and cover letter.
  • Emphasize your proficiency in programming languages like Python and SQL, as well as your statistical analysis skills.
  • Showcase your effective communication skills, especially your ability to convey technical concepts to non-technical stakeholders.
  • Demonstrate your experience with orchestrating complicated data pipelines and system engineering on large-scale datasets.
  • If applicable, mention any prior experience with Modeled Conversions, Ads Privacy, or Ads Measurement, as well as experience with Sequence Modeling, Reinforcement Learning, and Transformer Architecture.

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