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Staff Machine Learning Engineer, Content Visual AI
Pinterest. Set the technical direction and multi-quarter strategy for foundational ML capabilities across visual representation learning, multimodal content understanding, and LLM-based personalization .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in machine learning, particularly in visual representation learning, multimodal content understanding, and LLM-based personalization. Proven ability to lead technical initiatives, mentor teams, and drive cross-functional collaboration to translate foundational ML capabilities into impactful product outcomes.
Highest-signal resume keywords
Technical LeadershipLarge-Scale Distributed TrainingGenerative AI Model DevelopmentCross-Functional CollaborationData Pipeline Management
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningVisual Representation LearningMultimodal Content UnderstandingLLM-Based PersonalizationSelf-Supervised LearningSequence ModelingModeling TechniquesExperimentationProductionizationOperational Excellence
Soft Skills
Cross-Functional CommunicationCollaborationMentoring
Tools & Technologies
SparkHiveMapReduce
Certifications & Qualifications
MS or PhD in Computer ScienceMachine Learning
Industry Keywords
Generative ModelsData MiningResearch StrategyModel EvaluationContent UnderstandingSearch SystemsRecommendation Systems
Tech Stack
Tools & technologiesMapReduceSpark
About the role
Key responsibilities & impact- Set the technical direction and multi-quarter strategy for foundational ML capabilities across visual representation learning, multimodal content understanding, and LLM-based personalization
- Lead scientific and technical initiatives from problem definition and research strategy through experimentation, productionization, and adoption
- Build reusable embeddings, representations, semantic signals, and ML infrastructure
- Advance VLM, LLM, sequence modeling, self-supervised learning, retrieval, and distillation techniques
- Partner with Product, Data Science, Applied Science, and Engineering to translate foundational capabilities into product outcomes
- Influence cross-functional roadmaps and investments
- Establish practices for modeling, data pipelines, distributed training, inference, evaluation, experimentation, and operational excellence
- Mentor engineers and scientists
Requirements
What you’ll need- Minimum 7 years of industry experience, including 2+ years of tech leading teams
- MS or PhD degree in computer science, machine learning, or equivalent industry experience
- Publications at top machine learning, multimodal and/or data mining conferences or open-source ML experience
- Hands-on experience with large-scale distributed training of generative models, including LLMs, VLMs, and sequence models
- Hands-on experience with distributed tooling for ML data pipelines, such as Spark, Hive, and MapReduce
- Hands-on experience leading ambiguous ML and research efforts building, applying, and improving GenAI models for content understanding, search, and recommendation systems
- Technical leadership and experience setting direction for roadmaps spanning modeling, data pipelines, and productionization
- Excellent cross-functional communication and collaboration skills
Benefits
Comp & perks- Equity eligibility
- Flexible working model through PinFlex
- In-person collaboration only 1–2 times per quarter
- Relocation assistance is not provided