The opportunity
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…
What you'll do
Work with large scale structured and unstructured data, build and: continuously improve novel ML systems, product integrations, and performance optimizations 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 AI/ML models, drive engineering decisions, and quantify impact.
Work closely with other trust defense and platform teams to tackle the changing landscape of fraud attacks.
Hands-on productionize, and operate AI/ML solution and pipelines at scale,: including both batch and real-time use cases.
Lead, mentor, challenge and grow enthusiastic, collaborative AI/ML culture within the organization
+ years of industry experience in backend/platform engineering (or: equivalent) with BE/B.tech, preferably in CS, or equivalent qualification (experience in applied Machine Learning is a plus).
What they're looking for
- Strong programming (Python / Java or equivalent), DSA plus solid data engineering foundations.
- Understanding of ML best practices (eg. training/serving skew minimization,: A/B test, feature engineering, feature/model selection), algorithms (eg. gradient boosted trees, neural networks/deep learning, optimization) and domains (eg. natural language processing, computer vision, personalization and recommendation, anomaly detection)
- Experience with 3 or more of these technologies: Tensorflow, PyTorch, Kubernetes, Spark, orchestration (Airflow/Kubeflow), streaming/processing (Kafka/Spark/Ray), data warehouse (eg. Hive)
- Experience in building observability for AI systems (metrics/logging/traces),: with automated alerting, dashboards, and SLO management.
