Staff Machine Learning Engineer, Relevance and Personalization

Airbnb Airbnb · Consumer · United States · Software Engineering

Staff Machine Learning Engineer at Airbnb focused on relevance and personalization, responsible for search and recommendation systems. The role involves developing end-to-end ranking algorithms, building and productionizing ML models and pipelines at scale, and leveraging ML infrastructure for low-latency serving. The position requires significant industry experience in applied ML and strong data engineering skills.

What you'd actually do

  1. Work with large scale structured and unstructured data, build and continuously improve cutting edge Machine Learning models for Airbnb product, business and operational use cases.
  2. Work collaboratively with cross-functional partners including software engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements for machine learning models, drive engineering decisions, and quantify impact.
  3. Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases.
  4. Leverage third-party and in-house Machine Learning tools & infrastructure to develop reusable, highly differentiating and high-performing Machine Learning systems, enable fast model development, low-latency serving and ease of model quality upkeep.

Skills

Required

  • 9+ years of industry experience in applied Machine Learning
  • MS or PhD in relevant fields
  • Strong programming (Scala / Python / Java / C++ or equivalent)
  • data engineering skills
  • Deep understanding of Machine Learning best practices (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection)
  • algorithms (eg. neural networks/deep learning, optimization)
  • domains (eg. natural language processing, computer vision, personalization, search and recommendation, marketplace optimization, anomaly detection)
  • Experience with 3 or more of these technologies: Tensorflow, PyTorch, Kubernetes, Spark, Airflow (or equivalent), Kafka (or equivalent), data warehouse (eg. Hive)
  • Industry experience building end-to-end Machine Learning infrastructure
  • building and productionizing Machine Learning models
  • Exposure to architectural patterns of a large, high-scale software applications
  • Experience with test driven development
  • familiar with A/B testing, incremental delivery and deployment

What the JD emphasized

  • end-to-end ranking algorithms
  • productionize and operate Machine Learning models and pipelines at scale
  • low-latency serving

Other signals

  • end-to-end ranking algorithms
  • search and recommendation
  • productionize and operate Machine Learning models and pipelines at scale