Senior Machine Learning Engineer, Ads Content Understanding

Details

Reddit's Ads Content Understanding (ACU) team builds signals that describe what Reddit content is about, how brand safe and suitable it is, and what users are trying to accomplish in commercial conversations. ACU owns the Knowledge Graph, content taxonomies (IAB, Shopify Standard Product Taxonomy, IAS), opinion mining, shopping/product understanding, and a signals and tags registry that powers retrieval, ranking, safety, and insights across Ads Foundations and partner teams.

This role is an Applied Machine Learning Engineer position (IC4) focused on delivering robust, production ML systems that drive monetization impact. This is not a pure research scientist role: success is measured by shipped systems and business outcomes.

Responsibilities

• Operate across the full ML lifecycle: problem framing, data, modeling, evaluation, deployment, monitoring, and oncall; design scalable ML pipelines and champion responsible AI (bias, safety, explainability) for ACU models and signals in production.
• Provide technical leadership and mentorship to MLEs and SWEs doing ML work in ACU; lead design reviews, set technical standards, and uplift 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: define SLOs, alerting, and dashboards for key signals (coverage, latency, precision/recall, cost).
• Build and evolve content understanding capabilities for commercial conversations (reviews vs. recommendations vs. comparisons vs. Q&A; sentiment and stance; product entities and categories) and operationalize them as robust signals for contextual and shopping ads, auto-targeting, new formats, and insights products.
• 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

• 5+ years of relevant MLE experience delivering production ML systems (models + pipelines + serving) at scale, ideally in large-scale content understanding domains or Ads.
• Demonstrated senior-level technical leadership: contribution to architecture decisions, standards, and design reviews.
• Strong communication skills; ability to explain complex technical trade-offs to PMs, DSs, and other engineering teams in ambiguous, cross-team problem spaces.
• Experience building and shipping NLP / language models / content understanding models to production (e.g., classifiers, encoders, sequence or session models) with clear business outcomes (CTR/ROAS uplift, safety improvements). Commercial or intent modeling is a strong plus.
• Comfortable owning training, evaluation, and deployment code end-to-end and operating ML systems in production (including oncall responsibilities).

Preferred Qualifications

• Practical experience using LLMs in production for labeling, evaluation, or distillation (LM-as-judge, prompt-based classifiers, LLM-generated labels distilled into smaller models), including managing quality, cost, and latency trade-offs.
• Significant experience with PyTorch, TensorFlow, or similar, and production-quality code in Python; familiarity with at least one statically typed language (Go/Java/C++).
• Experience designing ML systems and pipelines: offline training, feature pipelines (batch/streaming), online serving, monitoring, and experimentation for high-traffic surfaces.

Benefits

• Comprehensive healthcare benefits and income replacement programs
• 401(k) with employer match
• Global benefit programs (workspace, professional development, caregiving support)
• Family planning support and gender-affirming care
• Mental health & coaching benefits
• Flexible vacation & paid volunteer time off
• Generous paid parental leave

Pay Transparency

• Base salary range (US-based): $216,700 - $303,400 USD
• This role may be eligible for equity (RSUs) and, depending on position, commission. Additional benefits for U.S.-based employees described above.

Other notes

• Role is listed as Remote - United States. Reddit offers flexible workforce arrangements and allows remote work in countries where it has a physical presence.
• Interview recordings/transcriptions by AI may be used in select roles; candidates can opt out prior to scheduled interviews.