Staff Machine Learning Engineer, Ads Conversion Core Modeling
Lead technical vision for Pinterest's Ads Conversion Core Modeling team. Build state-of-the-art large-scale DNN models for user action prediction, mine multi-modal signals for intent understanding, and mentor engineers while leveraging AI tools to accelerate development.
About the job
What you’ll do
- Lead the technical direction and development of state-of-the-art applied ML projects for ads conversion.
- Design and build large-scale DNN models to improve user action prediction with low latency.
- Mine text, visual, and user signals to better understand intention and infer interests from online activity.
- Use AI to accelerate analysis and iteration, while applying judgment and verification to ensure correctness and quality.
- Automate repeatable tasks such as documentation, reporting, and QA checks to speed up the development lifecycle.
- Coach and mentor engineers while collaborating with product and sales to design new ad products.
What we’re looking for
- Bachelor's degree in Computer Science, Statistics, or a related field.
- 6+ years of industry experience building production ML systems at scale (Search, Recommendations, or Ranking).
- 2+ years of experience leading technical projects or teams.
- Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs.
- Experience with Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring.
- Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration.
- High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final deliverables.
- Strong mathematical foundation and experience with statistical methods and A/B testing.
Skills
Machine Learning, Deep Neural Networks, Dnn, A/B Testing, Python, SQL, Large-Scale Ml Systems, Llm Tools, Ai Coding Assistants, Statistical Methods
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