Staff Machine Learning Engineer, Ads Content Understanding
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.
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: - Provide technical leadership and mentorship to MLEs and SWEs doing ML work in ACU, acting as de facto tech lead for content understanding and signals: driving design reviews, setting technical standards, and uplifting the team’s modeling and systems craft. - Develop evaluation systems and quality monitoring systems for content understanding signals, using SOTA LM-judge practices. - Drive operational excellence for ACU’s ML systems by defining SLOs, alerting, and dashboards for key signals (coverage, latency, precision/recall, cost) - Build and evolve content understanding capabilities for commercial conversations (e.g., reviews vs. recommendations vs. comparisons vs. Q&A; sentiment and stance; product entities and categories) and operationalize them as robust signals that power contextual and shopping ads, auto-targeting, new formats, and insights products. - Lead design and implementation of signals pipelines and produce an ACU signals registry. Partner with platform teams and other content understanding teams to ensure efficient, reliable serving at Reddit scale. - Drive LLM and modern ML best practices within ACU: define when to prompt, finetune, or distill; design evaluation and safety harnesses; and lead at least one major distillation effort to replace external APIs with in-house models. Requirements: - 7+ years of relevant MLE experience delivering production ML systems (models + pipelines + serving) at scale, ideally in large-scale content understanding domains, or Ads. - Demonstrated Staff-level technical leadership: has driven architecture decisions, standards, and design reviews across multiple teams, and has aligned PMs, DSs, and engineers on shared ML systems or platforms without direct people-management authority. - Excellent communication skills, with the ability to explain complex technical trade-offs to PMs, DSs, and other engineering teams, especially in ambiguous, cross-team problem spaces like Seekers/Searchers monetization. - Strong track record building and shipping NLP / Language models / content understanding models to production (e.g., classifiers, encoders, sequence or session models), with clear business outcomes (e.g., CTR/ROAS uplift, safety improvements). Experience with commercial or intent modeling is a strong plus. - Practical experience using LLMs in production for labeling, evaluation, or distillation (e.g., LM-as-judge, prompt-based classifiers, LLM-generated labels distilled into smaller models), including managing quality, cost, and latency trade-offs. - Deep experience with PyTorch, TensorFlow, or similar, and production-quality code in Python (and ideally one statically typed language like Go/Java/C++). Comfortable owning training, evaluation, and deployment code end-to-end. 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.
Day-to-day expectations
Reddit lists these responsibilities for the Staff Machine Learning Engineer, Ads Content Understanding role.
- Provide technical leadership and mentorship to MLEs and SWEs doing ML work in ACU, acting as de facto tech lead for content understanding and signals: driving design reviews, setting technical standards, and uplifting the team’s modeling and systems craft.
- Develop evaluation systems and quality monitoring systems for content understanding signals, using SOTA LM-judge practices.
- Drive operational excellence for ACU’s ML systems by defining SLOs, alerting, and dashboards for key signals (coverage, latency, precision/recall, cost)
- Build and evolve content understanding capabilities for commercial conversations (e.g., reviews vs. recommendations vs. comparisons vs. Q&A; sentiment and stance; product entities and categories) and operationalize them as robust signals that power contextual and shopping ads, auto-targeting, new formats, and insights products.
- Lead design and implementation of signals pipelines and produce an ACU signals registry. Partner with platform teams and other content understanding teams to ensure efficient, reliable serving at Reddit scale.
- Drive LLM and modern ML best practices within ACU: define when to prompt, finetune, or distill; design evaluation and safety harnesses; and lead at least one major distillation effort to replace external APIs with in-house models.
What a strong candidate brings
These requirements are extracted from the source listing and normalized for UpJobz readers.
- 7+ years of relevant MLE experience delivering production ML systems (models + pipelines + serving) at scale, ideally in large-scale content understanding domains, or Ads.
- Demonstrated Staff-level technical leadership: has driven architecture decisions, standards, and design reviews across multiple teams, and has aligned PMs, DSs, and engineers on shared ML systems or platforms without direct people-management authority.
- Excellent communication skills, with the ability to explain complex technical trade-offs to PMs, DSs, and other engineering teams, especially in ambiguous, cross-team problem spaces like Seekers/Searchers monetization.
- Strong track record building and shipping NLP / Language models / content understanding models to production (e.g., classifiers, encoders, sequence or session models), with clear business outcomes (e.g., CTR/ROAS uplift, safety improvements). Experience with commercial or intent modeling is a strong plus.
- Practical experience using LLMs in production for labeling, evaluation, or distillation (e.g., LM-as-judge, prompt-based classifiers, LLM-generated labels distilled into smaller models), including managing quality, cost, and latency trade-offs.
- Deep experience with PyTorch, TensorFlow, or similar, and production-quality code in Python (and ideally one statically typed language like Go/Java/C++). Comfortable owning training, evaluation, and deployment code end-to-end.
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
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Source: job-boards.greenhouse.io · Source ID: 7851761 · Confidence: 90/100 · Last checked: May 7, 2026
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