Staff Machine Learning Engineer, Content Quality Signals

<div class="content-intro"><p><strong>About Pinterest:</strong></p> <p>Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.</p> <p>Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the <a href="https://www.pinterestcareers.com/our-life/pinflex/">flexibility</a> to do your best work. Creating a career you love? It’s Possible.</p></div><p>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.</p> <p><strong><br>What you’ll do:</strong></p> <ul> <li>Lead modeling strategy for content understanding (vision, NLP, multimodal), including architecture selection, training approach, and evaluation methodology.</li> <li>Design and ship production models that generate content signals such as embeddings and classifications used across multiple product surfaces.</li> <li>Own the full ML lifecycle: data/labeling strategy (human labels + weak supervision), training pipelines, offline evaluation, online experimentation, deployment, and monitoring/retraining.</li> <li>Partner with infra/platform teams to ensure scalable, reliable training/serving (latency, cost, observability, rollout safety).</li> <li>Collaborate with signal-consuming teams (ranking, retrieval, integrity, ads) to define signal contracts, adoption patterns, and success metrics.</li> <li>Provide technical leadership through design reviews, mentoring, and raising the quality bar for modeling and ML engineering practices.</li> </ul> <p><strong><br>What we’re looking for:</strong></p> <ul> <li>M.S/ PhD degree in Computer Science, Statistics or related field.</li> <li>Significant industry experience building software and ML pipelines/systems, including technical leadership (project/tech lead or equivalent).</li> <li>Strong proficiency in Python and at least one ML stack such as PyTorch / TensorFlow, plus solid software engineering fundamentals.</li> <li>Proven experience training and deploying ML models to production, including model versioning, rollouts, monitoring, and retraining strategies.</li> <li>Deep hands-on experience in content understanding domains, such as:</li> <ul> <li>computer vision (classification, detection, representation learning),</li> <li>NLP (text classification, entity/topic modeling),</li> <li>multimodal / embedding models (e.g., transformer-based representations).</li> </ul> <li>Experience working with large-scale datasets and distributed compute (e.g., Spark-like ecosystems, distributed training, GPU environments).</li> <li>Strong applied skills in evaluation and experimentation: defining metrics, offline/online alignment, A/B testing, debugging regressions, and model quality analysis.</li> <li>Demonstrated ability to influence across teams and drive ambiguous problem areas to measurable outcomes.</li> </ul> <p> </p> <p><strong>Relocation Statement:</strong></p> <ul> <li>This position is not eligible for relocation assistance. Visit our<a href="https://www.pinterestcareers.com/pinflex/"> PinFlex</a> page to learn more about our working model.</li> </ul> <p> </p> <p><strong>In-Office Requirement Statement:</st ... (truncated, view full listing at source)