- Company Name
- Pinterest
- Job Title
- Machine Learning Engineer, Monetization Engineering
- Job Description
-
**Job Title**
Machine Learning Engineer, Monetization Engineering
**Role Summary**
Design, develop, and maintain large‑scale machine learning systems that power personalized advertising and product recommendation across Pinterest. Work closely with cross‑functional teams to iterate and deploy models that drive user engagement and monetization.
**Expectations**
- Minimum 2 years of industry experience applying machine learning to user modeling, personalization, recommendation, search, ranking, NLP, reinforcement learning, or graph representation learning.
- Proven ability to build end‑to‑end data pipelines, deploy scalable ML models, and operate in a high‑velocity product environment.
- Strong grasp of distributed computing frameworks (e.g., Hadoop, Spark) and experience with real‑time, stream‑based systems.
- Familiarity with contemporary recommendation and advertising systems.
- Ability to collaborate with product, engineering, and data science teams, communicating experiment results and model insights.
**Key Responsibilities**
- Architect and implement cutting‑edge deep‑learning and machine‑learning solutions for personalization and ad targeting.
- Partner with teams on Homefeed, Ads, Growth, Shopping, and Search to iterate on models, run experiments, and analyze impact.
- Leverage unique data assets to improve candidate retrieval and recommendation quality.
- Develop data processing pipelines that support training, evaluation, and deployment at scale.
- Stay current on industry trends in recommendation systems and large‑language modeling; evaluate and prototype new techniques.
- Contribute to quick experimentation cycles, from hypothesis to productionized model.
**Required Skills**
- Proficiency in machine‑learning methods: user modeling, personalization, recommender systems, ranking, search, NLP, RL, and graph learning.
- Hands‑on experience with Apache Hadoop, Spark, or equivalent distributed data platforms.
- Expertise in building scalable, end‑to‑end ML workflows: feature engineering, training, hyper‑parameter tuning, serving, and monitoring.
- Strong programming skills (Python, Scala/Java) and familiarity with ML libraries (PyTorch, TensorFlow, Scikit‑learn).
- Knowledge of ad‑tech concepts: ad ranking, targeting, retrieval, marketplace dynamics.
- Analytical mindset with ability to translate business KPIs into model objectives.
- Excellent written and verbal communication skills.
**Required Education & Certifications**
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, or a related field.
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San francisco, United states
Hybrid
Junior
31-12-2025