Senior Software Engineer (AI/ML), Trust

Role overview

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:

Everyone at Airbnb thinks about trust, but our team obsesses over it daily. At the core of trust is safety, and thus we spend a significant amount of our time and energy keeping the community safe. The Trust team is responsible for protecting our community and platform from fraud while also ensuring our hosts, guests, homes, and experiences meet our high standards. We constantly work to fight against online fraud (such as monetary loss, compromised accounts, spam and scam in messages, fake inventory, etc.) as well as offline fraud (theft, property damage, personal safety, etc.). We also work on onboarding and screening of users, and think about complex topics like identity and reputation to ensure that every interaction with Airbnb helps build trust in us and our community. Trust Engineering is responsible for the technology vision and development of a complex stack that runs on every key interaction on the platform.

The Difference You Will Make:

As part of the Trust Engineering team, you will work in a team of talented software engineers to help us build intuitive & delightful experiences, strengthen our current offerings, and deliver new products to strengthen our Trust defenses. You will play a significant role in shaping the technical vision and delivering flexible, efficient and scalable solutions to stay on top of the ever evolving attack vectors and make Airbnb the most safe and trusted community. There are still many unsolved problems to tackle in both online and offline risk, and we're looking for talented engineers to solve these challenges with us.

A Typical day:

• 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

Your Expertise:

• 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.