Sr Machine Learning Engineer

PayPal PayPal · Fintech · San Jose, CA +1 · Machine Learning Engineering

Sr. Machine Learning Engineer at PayPal focused on designing, developing, and implementing ML models and algorithms for various applications, including recommender systems and ranking. The role involves building scalable ML pipelines, deploying models into production, and collaborating with cross-functional teams to enhance services and customer experiences. Requires experience with ML frameworks, cloud platforms, and production ML systems.

What you'd actually do

  1. Develop and optimize machine learning models for various applications.
  2. Preprocess and analyze large datasets to extract meaningful insights.
  3. Deploy ML solutions into production environments using appropriate tools and frameworks.
  4. Collaborate with cross-functional teams to integrate ML models into products and services.
  5. Monitor and evaluate the performance of deployed models.

Skills

Required

  • 3+ years relevant experience and a Bachelor’s degree OR Any equivalent combination of education and experience.
  • Experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn.
  • Familiarity with cloud platforms (AWS, Azure, GCP) and tools for data processing and model deployment.
  • Several years of experience in designing, implementing, and deploying machine learning models.

Nice to have

  • Hands-on expertise with modern recommender architectures: two-tower retrieval, multi-task learning (MMoE, PLE), sequence/transformer models for user activity, and deep cross networks; fluent in PyTorch or TensorFlow
  • Experience building feed or home-screen ranking systems with multiple competing objectives (engagement, diversity, freshness, fairness) and low-latency serving (P99 < 100ms)
  • Production experience with graph neural networks (GraphSAGE, PinSage, LightGCN) or large-scale graph embeddings for social/interaction graphs — strongly preferred
  • Familiarity with contextual bandits, Thompson sampling, or RL for Next Best Action and notification problems
  • Strong ML systems fundamentals: distributed training, model serving, feature stores, and rigorous A/B testing
  • Prior work at a consumer social, marketplace, content, or fintech company (Meta, Pinterest, LinkedIn, YouTube, Snap, TikTok, Uber, DoorDash, Stripe, Block, Coinbase, or similar)
  • Clear technical communicator who can write design docs, influence cross-team decisions, and mentor engineers
  • PhD in CS, ML or related field strongly preferred; MS with strong industry depth also welcomed. Publications at NeurIPS, ICML, KDD, RecSys, SIGIR, or WWW are a plus

What the JD emphasized

  • low-latency serving (P99 < 100ms)
  • rigorous A/B testing

Other signals

  • deploy ML models into production
  • build scalable ML pipelines
  • recommender systems
  • low-latency serving
  • A/B testing