This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description 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. - 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. Qualifications - 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. Requirements - This position is not eligible for relocation assistance. - 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. Benefits - Visit our PinFlex page to learn more about our working model.

• Work closely in a small, cross-functional team on AI/ML systems • Collaborate with product managers to understand customer pain points • Deliver impactful solutions that support critical features • Build, deploy, and integrate machine learning models • Maintain high performance in extracting and classifying data

• Identifying high-leverage opportunities in incentive optimization, defining the technical roadmap, and driving end-to-end execution of ML/AI solutions to deliver measurable business impact. • Partnering closely with product managers, marketers, data scientists, and backend engineers to deeply understand business needs and build impactful ML applications. • Leading and mentoring the team, raising technical standards, and improving team execution efficiency.

• Contribute to the design, development, and deployment of robust machine learning models for production use in real-world security applications. • Develop within the full machine learning lifecycle; from problem definition to data pipeline design, model development, validation, deployment, and monitoring. • Establish and refine best practices in our ML system architecture, CI/CD pipelines for ML, and reproducible research methodologies. • Collaborate with cross-functional stakeholders including product managers, data engineers, and MLOps teams to ensure seamless model integration and delivery. • Perform advanced exploratory data analysis on large-scale sensory datasets (image, audio, radar, accelerometer) to derive insights and guide modeling strategies. • Stay ahead of industry advancements in machine learning, AI sensing, and signal processing, incorporating the latest innovations into Canopy’s technology stack. • Mentor and guide junior engineers and contribute to the hiring process and technical reviews.