Senior Machine Learning Manager - Ads Content Understanding

at Reddit
📍 United States
USD 266,000-372,400 per year
SENIOR
✅ Remote

SCRAPED

Used Tools & Technologies

Not specified

Required Skills & Competences ?

Go @ 4 Kafka @ 3 Kubernetes @ 3 Redis @ 3 Python @ 4 Spark @ 3 Java @ 4 Flink @ 3 Machine Learning @ 7 MLOps @ 3 TensorFlow @ 3 Communication @ 7 MongoDB @ 3 KubeFlow @ 3 MLFlow @ 3 API @ 4 Reporting @ 4 NLP @ 4 Cassandra @ 3

Details

Reddit is a community of communities built on shared interests, passion, and trust. The Ads Content Understanding team designs, builds, and selects state-of-the-art techniques to deliver interpretable signals associated with Reddit organic and business content (posts, comments, ads, creatives, linked content/media). Signals and content-understanding infrastructure produced by this team power Ads Marketplace AI, Community Intelligence Engine, Ads Insights capabilities, and core Reddit monetization products. The team focuses on Knowledge Graphs, Contextual Signals, Marketplace Efficiency for ads targeting/ranking/quality/measurement, and Business & Community Intelligence applications.

Responsibilities

  • Coach, motivate, build, hire, and lead a world-class team of ML practitioners, MLEs, SWEs, and data experts across US & EU geographies.
  • Lead, coordinate, and execute a coherent vision and roadmap for commercial content understanding capabilities to enable a fully context-aware Ads Monetization system (platform + marketplace).
  • Set and support a culture of data-informed decision making, with efficient processes and strong transparency.
  • Facilitate collaboration between different content-understanding platforms across Reddit, with focus on platform and signal consolidations.
  • Raise the bar across modeling and exploration phases in the content-understanding space.
  • Collaborate with cross-functional team leads (EMs, PMs, DSs) to translate business requirements into technical direction.

Requirements

  • 8+ years of industry experience as a Software Engineer (SWE), Data Scientist/Machine Learning Engineer (DSM/MLE), or similar.
  • 6+ years managing engineers and 2+ years managing MLEs.
  • Experience with at least two general-purpose programming languages such as Python, Go, Java, or C++.
  • Experience or familiarity with messaging and data infrastructure (examples listed by the team): Kafka, Amazon Simple Queue Service (SQS).
  • Experience or familiarity with data processing frameworks: Apache Spark, Apache Flink.
  • Experience or familiarity with key-value stores and document databases: Redis, DynamoDB, MongoDB, Cassandra.
  • Experience or familiarity with container orchestration and infrastructure: Kubernetes, Mesos.
  • Experience or familiarity with MLOps tooling (examples listed by the team): MLflow, TensorFlow, Kubeflow.
  • Strong written and verbal communication skills; ability to work effectively with product managers, data scientists, and other stakeholders.

Preferred Qualifications

  • Experience managing ML-heavy engineering teams.
  • Experience with Ads systems (targeting, ranking, quality, measurement).
  • Experience with Natural Language Understanding, NLP modeling and infrastructure (tokenization, NER, entity disambiguation).
  • Industry-relevant scientific contributions in NLP and content understanding.
  • Experience with LLMs in the context of multimodal content understanding.
  • Remote-friendly role; highly preferred candidates based near the SF Bay Area (Pacific Time) or NYC (Eastern Time).

Benefits

  • Comprehensive healthcare benefits.
  • 401(k) matching.
  • Workspace benefits for home office.
  • Personal and professional development funds.
  • Family planning support.
  • Flexible vacation and Reddit Global Wellness Days.
  • 4+ months paid parental leave.
  • Paid volunteer time off.

Pay Transparency

  • Base pay range for this US-based position: $266,000 - $372,400 USD.

Additional Notes

  • Team focus areas explicitly include: Knowledge Graph (semi-automatic curation, expansion, entity-level disambiguation), contextual signals for posts/landing pages/ads, marketplace efficiency (targeting/ranking/quality/measurement), and business & community intelligence applications (APIs, reporting for advertisers).