About the company
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.
Responsibilities
- 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.
Requirements
- 7+ 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).
- 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.
- Industry experience building end-to-end Machine Learning infrastructure and/or building and productionizing Machine Learning models is a plus.
- Must have experience working in large tech product companies solving real world problems.
- 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.
- Experience with the Trust and Risk domain is a plus.
Conditions
- Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range shown below is annualized, is inclusive of allowances and is subject to change and may be modified in the future. This role may also be eligible for bonus or incentives, one or more equity programs, benefits, and Employee Travel Credits.