À propos
As a Senior Machine Learning Engineer on the Personalization Platform team, you will design, build, and optimize large-scale, real-time personalization and recommendation systems that power key customer experiences across PayPal. You will work closely with data scientists, product managers, and platform engineers to productionize models, enhance infrastructure, and advance the capabilities of PayPal's personalization ecosystem. This is a hands-on role focused on delivering high-quality ML systems, contributing to technical design, and driving engineering excellence across the team. You will develop scalable ML pipelines and real-time inference services, implement robust data and feature workflows, and transform experimental models into reliable production solutions. You will help shape distributed systems that support low-latency personalization at scale, champion best practices in testing, CI/CD, observability, and operational quality, and ensure strong system performance through ongoing monitoring and feedback loops. Through close collaboration with cross-functional partners, you will enable seamless end-to-end deployments and help deliver impactful, customer-first personalization experiences. Essential Responsibilities: Develop and optimize machine learning models for various applications. Preprocess and analyze large datasets to extract meaningful insights. Deploy ML solutions into production environments using appropriate tools and frameworks. Collaborate with cross-functional teams to integrate ML models into products and services. Monitor and evaluate the performance of deployed models. Expected Qualifications: 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. Additional Responsibilities & Preferred Qualifications: 3 years of experience in machine learning engineering or applied ML development. Experience building and scaling ML systems in production, particularly related to personalization, recommendations, ranking, or embeddings. Strong software engineering background with proficiency in Python, Java, or Scala. Hands-on experience with distributed systems and streaming technologies such as Kafka, Flink, Spark, or similar. Solid understanding of ML model lifecycle, including training, deployment, monitoring, and continuous improvement. Experience working with cloud-native architectures and modern CI/CD pipelines. Ability to collaborate effectively in cross-functional teams and communicate clearly with technical stakeholders. Preferred Qualifications: Experience with real-time inference platforms or feature stores. Familiarity with model monitoring, drift detection, and MLOps tooling. Exposure to deep learning frameworks (TensorFlow, PyTorch) or large-scale embeddings. Background with containerization and orchestration (Docker, Kubernetes). Experience supporting experimentation pipelines or A/B testing at scale.
Compétences linguistiques
- English
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