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.

Relocation Statement:

• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

In-Office Requirement Statement:

• We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.
• This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the country.

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