- Company Name
- Pinterest
- Job Title
- Machine Learning Engineer
- Job Description
-
**Job title**
Machine Learning Engineer
**Role Summary**
Design, develop, and deploy large‑scale machine learning models to personalize content and improve recommendation quality across product surfaces such as Homefeed, Ads, Growth, Shopping, and Search. Build and maintain data pipelines and scalable ML systems, collaborate with cross‑functional teams, monitor model performance, and iterate for continuous improvement.
**Expectations**
- Deliver end‑to‑end ML solutions that run reliably in production and handle large, streaming datasets.
- Work closely with product managers, data scientists, and engineers to experiment, iterate, and optimize algorithms.
- Communicate findings clearly, document experiments, and share knowledge with the team.
**Key Responsibilities**
1. Design, build, and evaluate advanced ML models for personalization, recommendation, ranking, NLP, reinforcement learning, or graph representation.
2. Develop and maintain robust data pipelines using Hadoop, Spark, and other big‑data tools to support training and serving.
3. Deploy models to production, set up monitoring, troubleshoot issues, and conduct A/B testing to measure impact.
4. Optimize model infrastructure for scalability, latency, and throughput in real‑time environments.
5. Stay current on research and industry trends, assess applicability to Pinterest’s product ecosystem.
6. Create clear documentation of methodologies, experiments, and results.
**Required Skills**
- 2+ years industry experience in ML applications (personalization, recommendation, ranking, NLP, RL, graph learning).
- Hands‑on building data pipelines and large‑scale ML systems.
- Proficiency in Python and a JVM language (Java or Scala).
- Experience with Hadoop, Spark, and distributed data processing.
- Deployment of ML models into production (MLflow, TensorFlow Serving, etc.).
- Strong analytical ability, comfort interpreting large datasets to guide model development.
- Excellent collaboration and communication skills.
- Passion for applied ML and product impact.
**Required Education & Certifications**
- Bachelor’s degree in Computer Science, Electrical Engineering, Statistics, or related field.
- Preferred: Master’s or Ph.D. in Machine Learning, CS, or related domain.
- Publications at top ML conferences or recognized community contributions preferred but not mandatory.