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Jobs/Remote (United States)/Staff Machine Learning Engineer, Ads Measurement Modeling
Remote (United States), US

Staff Machine Learning Engineer, Ads Measurement Modeling

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.

Company
Reddit
Compensation
Not listed
Schedule
Full-Time
Role overview

What this role actually needs.

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. Responsibilities: - Lead the technical strategy and architecture for our company’s ads identity modeling solutions and other related ads measurement models - Design and train advanced ML models while ensuring accuracy, scalability, and compliance with privacy requirements, managing trade-offs between complexity, latency, and prediction quality - Oversee end-to-end ML workflows—from data ingestion and feature engineering to model training, evaluation, and deployment—optimizing for performance and cost - Partner with cross-functional teams (e.g., product management, data science, platform engineering, privacy, legal) to define the roadmap and set long-term goals - Establish engineering best practices, code quality standards, and data governance guidelines to ensure maintainability and trustworthiness of the identity graph - Mentor and coach junior engineers, fostering a culture of innovation, technical excellence, and knowledge sharing across the organization Requirements: - 7+ years of professional software engineering experience, with at least 3+ years focused on ML-driven systems at scale - Demonstrated experience architecting and building ads measurement modeling solutions leveraging advanced machine learning techniques - Strong knowledge of various identifiers (cookies, hashed emails, phone numbers, IP addresses, user agents) and their use in identity resolution - Proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries for feature engineering, model training, and inference - Solid understanding of large-scale data processing, distributed computing, and data infrastructure (e.g., Spark, Kafka, Beam, Flink) - Proven technical leadership in cross-functional settings, driving architectural decisions and influencing stakeholders (product, data science, privacy, legal) Benefits: - Comprehensive Healthcare Benefits and Income Replacement Programs - 401k with Employer Match - Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support - Family Planning Support - Gender-Affirming Care - Mental Health & Coaching Benefits Company context: Reddit builds large-scale consumer, ads, and platform systems with hiring across mobile, backend, machine learning, and product engineering.

Responsibilities

Day-to-day expectations

Reddit lists these responsibilities for the Staff Machine Learning Engineer, Ads Measurement Modeling role.

  • Lead the technical strategy and architecture for our company’s ads identity modeling solutions and other related ads measurement models
  • Design and train advanced ML models while ensuring accuracy, scalability, and compliance with privacy requirements, managing trade-offs between complexity, latency, and prediction quality
  • Oversee end-to-end ML workflows—from data ingestion and feature engineering to model training, evaluation, and deployment—optimizing for performance and cost
  • Partner with cross-functional teams (e.g., product management, data science, platform engineering, privacy, legal) to define the roadmap and set long-term goals
  • Establish engineering best practices, code quality standards, and data governance guidelines to ensure maintainability and trustworthiness of the identity graph
  • Mentor and coach junior engineers, fostering a culture of innovation, technical excellence, and knowledge sharing across the organization
Requirements

What a strong candidate brings

These requirements are extracted from the source listing and normalized for UpJobz readers.

  • 7+ years of professional software engineering experience, with at least 3+ years focused on ML-driven systems at scale
  • Demonstrated experience architecting and building ads measurement modeling solutions leveraging advanced machine learning techniques
  • Strong knowledge of various identifiers (cookies, hashed emails, phone numbers, IP addresses, user agents) and their use in identity resolution
  • Proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries for feature engineering, model training, and inference
  • Solid understanding of large-scale data processing, distributed computing, and data infrastructure (e.g., Spark, Kafka, Beam, Flink)
  • Proven technical leadership in cross-functional settings, driving architectural decisions and influencing stakeholders (product, data science, privacy, legal)
Benefits

Why people would want this job

Reddit published these compensation, benefits, or working-context details with the role.

  • Comprehensive Healthcare Benefits and Income Replacement Programs
  • 401k with Employer Match
  • Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
  • Family Planning Support
  • Gender-Affirming Care
  • Mental Health & Coaching Benefits
UpJobz market context

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Staff Machine Learning Engineer, Ads Measurement Modeling is framed against UpJobz source checks, country scope, compensation visibility, and work-authorization signals so candidates can make a faster go/no-go decision.

United States tech market

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Work authorization read

Current extracted signal: United States residents. UpJobz treats this as a search signal, not legal advice, and links visa-sensitive roles back to the relevant visa hub where possible.

Location read

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Interview themes

Artificial IntelligenceRemoteaillmmachine-learningdata

Watchouts

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aillmmachine-learningdataproductplatformapimobilebackend
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Source: job-boards.greenhouse.io · Source ID: 7890096 · Confidence: 90/100 · Last checked: May 7, 2026

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