Staff Machine Learning Engineer, Relevance and Personalization

Responsibilities

• Develop and continuously improve machine learning models for product, business, and operational use cases using large-scale structured and unstructured data.
• Collaborate with software engineers, product managers, operations, and data scientists to identify opportunities, prioritize machine learning requirements, make engineering decisions, and quantify impact.
• Develop, productionize, and operate machine learning models and pipelines at scale for batch and real-time use cases.
• Use third-party and in-house machine learning tools and infrastructure to enable reusable systems, rapid model development, low-latency serving, and maintainable model quality.
• Build end-to-end ranking algorithms and ecosystems for search, recommendation, personalization, and marketplace optimization.
• Work on areas such as feature platforms, model interpretability, hyperparameter optimization, and concept drift detection.

Requirements

• 9+ years of industry experience in applied machine learning, including an MS or PhD in a relevant field.
• Strong programming and data engineering skills using Scala, Python, Java, C++, or equivalent.
• Deep understanding of machine learning best practices, including training/serving skew minimization, A/B testing, feature engineering, and feature and model selection.
• Knowledge of neural networks, deep learning, and optimization.
• Experience with relevant domains such as natural language processing, computer vision, personalization, search and recommendation, marketplace optimization, or anomaly detection.
• Experience with at least three of TensorFlow, PyTorch, Kubernetes, Spark, Airflow or equivalent, Kafka or equivalent, and data warehouses such as Hive.
• Experience building end-to-end machine learning infrastructure and/or productionizing machine learning models.
• Experience with architectural patterns for high-scale software applications, including well-designed APIs, high-volume data pipelines, and efficient algorithms and models.
• Familiarity with test-driven development, A/B testing, incremental delivery, and deployment.

Compensation

• Base pay range: $212,000–$265,000 USD.
• May also be eligible for bonus, equity, benefits, and employee travel credits.