Staff Machine Learning Engineer, Content Quality Signals

The Content Understanding team builds machine learning models that “read” Pinterest content—images, text, and video—to produce high-quality semantic signals (e.g., embeddings, localization, quality/safety labels). These signals power relevance and retrieval for Homefeed, Search, Related Pins, and Ads, and also support integrity use cases like spam and low-quality detection. We work end-to-end: from data and labeling strategy, to model training and evaluation, to low-latency serving and monitoring at Pinterest scale. The role is ideal for a senior modeler who also enjoys developing, productionizing models and leading technical direction across teams.

What you’ll do:

• Lead modeling strategy for content understanding (vision, NLP, multimodal), including architecture selection, training approach, and evaluation methodology.
• Design and ship production models that generate content signals such as embeddings and  classifications used across multiple product surfaces.
• Own the full ML lifecycle: data/labeling strategy (human labels + weak supervision), training pipelines, offline evaluation, online experimentation, deployment, and monitoring/retraining.
• Partner with infra/platform teams to ensure scalable, reliable training/serving (latency, cost, observability, rollout safety).
• Collaborate with signal-consuming teams (ranking, retrieval, integrity, ads) to define signal contracts, adoption patterns, and success metrics.
• Provide technical leadership through design reviews, mentoring, and raising the quality bar for modeling and ML engineering practices.

What we’re looking for:

• M.S/ PhD degree in Computer Science, Statistics or related field.
• Significant industry experience building software and ML pipelines/systems, including technical leadership (project/tech lead or equivalent).
• Strong proficiency in Python and at least one ML stack such as PyTorch / TensorFlow, plus solid software engineering fundamentals.
• Proven experience training and deploying ML models to production, including model versioning, rollouts, monitoring, and retraining strategies.
• Deep hands-on experience in content understanding domains, such as:
• computer vision (classification, detection, representation learning),
• NLP (text classification, entity/topic modeling),
• multimodal / embedding models (e.g., transformer-based representations).
• Experience working with large-scale datasets and distributed compute (e.g., Spark-like ecosystems, distributed training, GPU environments).
• Strong applied skills in evaluation and experimentation: defining metrics, offline/online alignment, A/B testing, debugging regressions, and model quality analysis.
• Demonstrated ability to influence across teams and drive ambiguous problem areas to measurable outcomes.
• Nice to have:
  • Experience with Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, te