Airbnb Senior Staff Machine Learning Engineer 

United States 

702860337

The Difference You Will Make:

As a machine learning engineer or applied scientist, your expertise will be pivotal in developing Conversational AI solutions and other cutting-edge AI techniques to define and shape the future of the Airbnb Community Support experience. You will also partner with product managers, software engineers, and operation teams to leverage AI and engineering innovations to simplify the business requirements into scalable solutions.

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:

• 10+ years of industry experience in applied Machine Learning, inclusive MS or PhD in relevant fields.
• Deep understanding of Machine Learning best practices (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (eg. neural networks/deep learning, optimization) and domains (eg. NLP, computer vision, personalization, search and recommendation, marketplace optimization, anomaly detection).
• Strong programming (Scala / Python / Java / C++ or equivalent) and data engineering skills.
• Experience with 3 or more of these technologies: PyTorch, Tensorflow, Kubernetes, Spark, Airflow (or equivalent), Kafka (or equivalent), data warehouse (eg. Hive).
• Industry experience building end-to-end Machine Learning infrastructure and/or building and productionizing Machine Learning models.
• Experience with applying LLMs, prompt design, and fine-tuning methods.
• 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.

Offices: United States