R
Staff Machine Learning Engineer (App Ads Modeling)
Skills
Deep LearningPyTorchTensorFlowMachine Learning
13 days ago
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Important information
Contract type:
Permanent
Salary:
$230,000 – $322,000 / year
Location:
United States
Work mode:
Onsite
Published on:
7 October 2026
What they need
- We’re looking for a Staff-level Machine Learning Engineer to join Reddit’s Ads Ranking Org. This role is part of a broader effort to enhance Reddit’s ML-powered ad ranking systems through improved model architectures, contextual embeddings, and conversion-optimized modeling
- This position will sit on the App Ads Modeling team and report directly into the Ranking Org
- Design and train advanced ML models (e.g., DNNs, transformers) to power Reddit Ads Ranking
- Develop and optimize features including embeddings, contextual signals, and cross-session behavior
- Work closely with product, infra, and data teams to drive end-to-end model deployment and performance analysis
- Mentor other MLEs and contribute to modeling best practices across the org
- Shape the long-term modeling vision across one or more domains (conversion, app ads, shopping, brand, etc.)
Benefits
- Comprehensive health benefits
- Flexible vacation & Reddit global days off
- Family planning funds & 4+ months paid parental leave
- Personal & professional development funds
- Paid volunteer time off
- Workspace & home office benefits- The ideal candidate will bring strong end-to-end modeling expertise, a deep understanding of modern deep learning techniques, and a passion for building systems that drive measurable impact in ads marketplaces
- While ads experience is a plus, strong modelers from recommendation systems, search relevance, or other performance-driven domains are encouraged to apply
- Knowledge about Recsys or Ads funnel
- 7+ years of industry experience, including several years in applied ML roles
- Strong experience with Deep Learning architectures and ML frameworks (TensorFlow, PyTorch)
- State of the Art models for Ads or Recsys experience (ex: Transformers, Recsys)
- Experience working with large-scale datasets and complex feature pipelines
- Comfort collaborating with infra and data teams on feature serving and training pipelines
- Strong background in recommendation systems, ads ranking, or similar domains
- Track record of delivering high-impact models in production settings
- Strong problem-solving and experimentation skills
- Experience in ads marketplaces at peer companies
- Publications, patents, or industry contributions in applied ML or ranking systems
- Experience with conversion modeling, app ads, or performance/brand ads
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