Senior Machine Learning Engineer, Relevance

NewInactive

AirbnbTravel marketplace

This position is US - Remote Eligible. ... you must live in a state where Airbnb, Inc. has a registered entity.Full-TimeSenior

This job is no longer active. We keep the page for reference, but the employer may not accept new applications.

Job Details

Experience: 5+ years of industry experience in applied Machine Learning
Required Skills: PythonJavaKubernetesMachine LearningPyTorchC++AirflowSparkTensorflowScala

Have 5+ years of industry experience in applied machine learning.
Have an MS or PhD in a relevant field.
Have strong programming skills in Scala, Python, Java, C++, or equivalent, and data engineering skills.
Understand machine learning best practices, including training/serving skew minimization, A/B testing, feature engineering, and feature/model selection.
Understand machine learning algorithms such as neural networks, deep learning, and optimization.
Have expertise in relevant domains such as natural language processing, computer vision, personalization, search and recommendation, marketplace optimization, or anomaly detection.
Have experience with at least three of TensorFlow, PyTorch, Kubernetes, Spark, Airflow or equivalent, Kafka or equivalent, and data warehouses such as Hive.
Have industry experience building end-to-end machine learning infrastructure and/or building and productionizing machine learning models.
Have exposure to architectural patterns for large-scale software applications, including well-designed APIs and high-volume data pipelines.
Have experience with test-driven development and familiarity with A/B testing, incremental delivery, and deployment.

Build and continuously improve machine learning models for Airbnb 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 for business impact.
Refine and prioritize machine learning model requirements and drive engineering decisions.
Quantify the impact of machine learning models and solutions.
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 build reusable systems.
Support fast model development, low-latency serving, and model quality upkeep.