Staff Machine Learning Engineer

Reddit Reddit · Consumer · San Francisco, CA · Engineering

Staff Machine Learning Engineer at Reddit focused on designing, developing, and training advanced ML models for large-scale online advertising ranking and optimization. The role involves end-to-end model lifecycle execution, feature representation, experimentation, deployment, monitoring, and providing technical leadership. The primary deliverable is an AI-powered product for advertising.

What you'd actually do

  1. Design, develop, and train advanced machine learning models, including deep neural networks, transformer-based architectures, and reinforcement learning systems, to power large-scale online advertising ranking and optimization platforms.
  2. Lead the development and optimization of complex feature representations, including high-dimensional embeddings, contextual and temporal signals, and cross-session user behavior modeling.
  3. Drive end-to-end model lifecycle execution, including system architecture design, large-scale experimentation, model deployment, performance monitoring, and iterative infrastructure improvements in production environments.
  4. Collaborate closely with product, data, and infrastructure engineering teams to translate business objectives into scalable, statistically rigorous modeling solutions.
  5. Conduct advanced experiment design and causal analysis to evaluate model impact and inform strategic decisions.

Skills

Required

  • Python
  • Java
  • Scala
  • C++
  • Go
  • Rust
  • major machine learning frameworks and libraries
  • applied statistics
  • hypothesis testing
  • experiment design for online machine learning systems
  • large-scale data processing and analytics frameworks
  • deployment and operation of production systems in containerized and distributed environments
  • Designing and training advanced models, including deep neural networks, transformer-based architectures, and reinforcement learning models
  • marketplace dynamics, such as real-time bidding (RTB) or pacing control systems
  • developing and optimizing online advertising systems, including ad ranking, targeting, and market place
  • providing technical leadership, mentorship, or guidance to other machine learning engineers

What the JD emphasized

  • advanced machine learning models
  • deep neural networks
  • transformer-based architectures
  • reinforcement learning systems
  • large-scale online advertising ranking and optimization platforms
  • complex feature representations
  • high-dimensional embeddings
  • contextual and temporal signals
  • cross-session user behavior modeling
  • end-to-end model lifecycle execution
  • system architecture design
  • large-scale experimentation
  • model deployment
  • performance monitoring
  • iterative infrastructure improvements
  • production environments
  • statistically rigorous modeling solutions
  • advanced experiment design
  • causal analysis
  • technical leadership
  • mentorship
  • modeling standards
  • best practices
  • long-term technical strategy
  • modeling vision
  • conversion optimization
  • application advertising
  • shopping
  • brand advertising

Other signals

  • large-scale online advertising ranking and optimization platforms
  • end-to-end model lifecycle execution
  • technical leadership and mentorship