Staff Machine Learning Engineer, Anti-Bots

Airbnb was born in 2007 when two Hosts welcomed three guests to their San Francisco home, and has since grown to over 4 million Hosts who have welcomed more than 1 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: 

Are you passionate about using AI for the greater good? Are you interested in protecting our Airbnb community from malicious bots? Do you have an adversarial mindset and a blend of quantitative, ML, and deep learning skills? 

We are hiring a Staff Machine Learning Engineer to be a founding member of our new Anti-Bots Engineering team. We are an enthusiastic team spirited about constructing online machine learning models to deter data scraping. We're harnessing the power of AI, state of the art ML techniques, and data for the higher purpose of preventing scraping and protecting data.

In this opportunity, you will join forces with your Manager, Tech Lead, Senior Software Engineers, as well as other engineering, analytics and business teams. We have the creativity and excitement of a start-up with the foundation of a well-established brand. We are fueled by the passion to use our technical prowess to start a new team, and we welcome individuals with the same drive to join us.

The Difference You Will Make:

As a Staff Machine Learning Engineer in this role, you will be working across technologies, codebases, and partner teams to understand complex systems from top to bottom. You will be using state of the art ML techniques and continuously evolving our capabilities to detect and disrupt attackers. You'll be involved in researching attack patterns and developing models to identify traffic that matches these patterns. You'll also be partnering with various teams to implement interventions against these attackers.

Successful candidates for this role are hands-on ICs who enjoy coding. You will need a strong background in deep learning and ML, an adversarial mindset, and a passion for using ML to make a positive impact on the world.

A Typical Day: 

• Rely on your extensive domain knowledge of ML and deep learning to proactively detect attacks against our platform.
• Analyze our traffic data to identify attack patterns. Develop and implement tools to combat attacks. Implement effective countermeasures to stop scraping based on these identified patterns.
• 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.
• Prototype Machine Learning use cases for use in the product, and work with stakeholders to iterate on requirements. Execute on state of the art ML techniques from scratch.
• Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases.
• Leverage third-party or open source 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.

Your Expertise:

• 9+ years of industry experience in applied Machine Learning with a BS/Masters or 6+ years with a PhD
• Strong programming (Scala / Python / Java/ C++ or equivalent) skills for hands-on IC work
• Deep understanding of Machine Learning best practices (e.g. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (e.g. gradient boosted trees, neural networks/deep learning, optimization) and domains (e.g. natural language processing, computer vision, personalization and recommendation, anomaly detection); proficiency with deep learning techniques
• Hands-on ‘builder’ experience with experience in any of these domains (eg. anti-bots, fraud detection, trust and safety, search relevance, natural language processing, computer vision, personalization and recommendation, anomaly detection, ads or similar)
• Industry experience productionalizing Machine Learning models
• Experience with technologies such as Tensorflow, PyTorch, Kubernetes, Spark, Airflow (or equivalent), Kafka (or equivalent), and data warehouse (e.g. Hive)
• Experience with test driven development, familiar with A/B testing, incremental delivery and deployment

Nice to Have:

• Strong data engineering and/or analytics skills
• Industry experience building end-to-end Machine Learning systems