Machine Learning Engineer, Relevance and Personalization

Airbnb·United Statesmid

En corto

→Ingeniero de Machine Learning que construye modelos de recomendación y búsqueda a gran escala en Airbnb.
→Día a día: desarrollas, produciones y operas modelos ML con datos estructurados y no estructurados, colaborando con equipos multidisciplinarios.
→Destaque: trabajas en sistemas que impactan directamente la experiencia de millones de huéspedes y anfitriones en todo el mundo.

Experiencia trabajando con equipos internacionales o habilidades en inglés (como requisito

En ~1 minuto te damos: quién te entrevista, las preguntas probables con respuestas desde tu CV, y tu CV adaptado a esta vacante. Gratis, sin tarjeta.

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¿Qué piden?

✓Título de doctorado en ML/AI o 2+ años de experiencia en industria con M.S. o B.S.
✓Habilidades sólidas en programación (Python, Scala, Java, C++) y engineering de datos.
✓Conocimiento profundo en ML: entrenamiento/servicio, A/B testing, ingeniería de características, redes neuronales.
✓Experiencia con 3+ tecnologías: TensorFlow, PyTorch, Kubernetes, Spark, Airflow, Kafka, Hive.
✓Capacidad para colaborar con ingenieros, PMs y datos en proyectos de impacto real.
✓Exposición a arquitecturas de software a gran escala y pipelines de datos de alto volumen.

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

TensorFlowPyTorchKubernetesSparkAirflowKafkaHiveScalaPythonJava

¿A quién escribirle en Airbnb?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. The Community You Will Join: The Relevance and Personalization team at Airbnb is responsible for search and recommendation across the entire Airbnb digital platform. Be a leader in the team working on critical, impactful projects with focus on developing end-to-end ranking algorithms and ecosystems for optimizing multiple critical business objectives. The Difference You Will Make: We build cutting-edge AI technologies across the end-to-end search ranking product stack w.r.t. data pipelines, feature and model innovations, serving and experimentation efficiency, leveraging rich signals from various types of data (structured, sequential, image, text, etc) at Airbnb. We collaborate closely with teams across Airbnb to develop the ranking solutions and support a healthy marketplace for hosts and guests to further Airbnb’s mission of creating a world where people can Belong Anywhere. Some past publications from the team can be found here: https://sites.google.com/view/airbnb-relevance-publications/home A Typical Day: • 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. Your Expertise: • New grad Ph.D in ML/AI or 2+ years of industry experience in applied ML/AI with a M.S. or B.S degree. • Strong programming (Scala / Python / Java / C++ or equivalent) and data engineering skills. • Deep understanding of Machine Learning best practices (e.g. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (e.g. neural networks/deep learning, optimization) and domains (eg. natural language processing, computer vision, personalization, search and recommendation, marketplace optimization, anomaly detection). • Exposure to 3 or more of these technologies: Tensorflow, PyTorch, Kubernetes, Spark, Airflow (or equivalent), Kafka (or equivalent), data warehouse (eg. Hive). • Exposure to architectural patterns of large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models). • Proven abili

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