Staff Machine Learning Engineer

Pinterest Pinterest · Consumer · Palo Alto, CA · Monetization

Staff Machine Learning Engineer focused on Agentic AI & Recommendations for advertiser and seller experiences. Will lead ML strategy and execution for intelligence layers, building recommendation systems, context foundations, and feedback loops for AI agents. Requires deep recommendation systems expertise combined with modern agentic AI to shape advertiser and seller workflows.

What you'd actually do

  1. Lead the design and implementation of large-scale recommendation and decisioning systems that power proactive advertiser and seller guidance across Ads Manager, Pinterest Business Assistant, Pinnacle, and sales productivity workflows.
  2. Build ML foundations for a unified context layer and context agent that transforms campaign, account, performance, market, workflow, and interaction data into reusable signals for agentic experiences.
  3. Own recommendation initiatives end-to-end, from problem framing, label and feedback design, feature pipelines, model development, and offline evaluation through production deployment, experimentation, and monitoring.
  4. Develop evaluation and feedback loops that measure recommendation quality, user trust, action rates, business impact, and failure modes, then use those learnings to continuously improve models and agent behavior.
  5. Apply modern ML techniques such as retrieval and ranking, embeddings, personalization, multi-objective optimization, contextual decisioning, and response modeling to business-critical advertiser and seller workflows.

Skills

Required

  • 7+ years of experience building and deploying large-scale ML systems in production
  • strong end-to-end ownership from problem scoping through evaluation and experimentation
  • solid software engineering skills in at least one modern language (e.g., Python, Java)
  • large-scale data systems
  • Degree in Computer Science, Mathematics, or a related technical field, or equivalent experience
  • Strong end-to-end ML ownership, including problem scoping, data and label design, feature engineering, model training, production deployment, offline/online evaluation, experimentation, and monitoring.
  • Deep understanding of recommendation system architectures such as candidate generation, retrieval, ranking, re-ranking, embeddings, vector search, multi-task learning, calibration, contextual bandits, or reinforcement learning.
  • Proven Staff-level technical leadership as a hands-on IC, setting technical direction and driving multi-quarter ML and systems roadmaps, including aligning stakeholders on priorities, trade-offs, and execution plans.
  • Excellent cross-functional communication and collaboration skills, building strong partnerships with product, data science, infra, and partner ML teams to clarify ambiguous problem spaces, co-create solutions, and drive consensus with senior stakeholders.
  • Experience using AI coding assistants (e.g., Cursor, Claude Code) and LLM-powered productivity tools to accelerate development, experimentation, and data exploration, with a clear approach to validation, data protection, and critical review of AI-assisted work.

What the JD emphasized

  • Staff-level technical leadership
  • end-to-end ownership
  • large-scale ML systems
  • Agentic AI
  • recommendation systems

Other signals

  • Agentic AI
  • Recommendation Systems
  • Large-scale ML systems
  • Production Deployment
  • Staff-level IC