Airbnb

Staff Machine Learning Engineer, Relevance and Personalization

Vaga remota de Machine Learning Engineering com fit claro de localização do candidato.

Publicada5 de jul. de 2026

Países elegíveis1 país aceito

Sinal de senioridadeSenior

Modelo de trabalhoRemoto

Locais aceitos para candidatos

Estados Unidos

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Países aceitos para candidatos estão listados (1).

Atualidade da fonte5 de jul. de 2026

Fit de localização1 país aceito

Match de stackJava, Kubernetes

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Aplicando paraStaff Machine Learning Engineer, Relevance and PersonalizationAirbnb

Fit de país1 país aceito

Caminho de aplicaçãoSite da empresa

WithMiraSalve ou assine antes de sair

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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.
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.
Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases.
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.
Example projects include: feature platform, model interpretability, hyperparameter optimization, concept drift detection.

5+ years of industry experience in applied Machine Learning, inclusive MS or PhD in relevant fields
Strong programming (Scala / Python / Java / C++ or equivalent) and 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) and 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 and/or building and productionizing Machine Learning models
Exposure to architectural patterns of a large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models)
Experience with test driven development, familiar with A/B testing, incremental delivery and deployment.

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