Staff Machine Learning Engineer, BizTech

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:

The CRM Engineering team builds world-class tools that drive growth and foster meaningful  relationships between Airbnb and its community, including Hosts, Guests, Law Enforcement, Government Regulators, Local Community Leaders, and more. This team tackles some of the most challenging and unique problems faced by our business. Here are a few examples:

• Growth Platform: Designed to increase supply and retain existing hosts.
• Policy Platform: Enables Airbnb to engage with regulatory bodies across the globe regarding short-term rental regulations. This platform tracks regulations that impact the Airbnb business and provides intelligence to address unique business challenges.
• Community Platform: Establishes a social platform to drive community enablement, collaboration, and success.
• Agent Tools: Empowers agents to understand and resolve issues that customers are unable to address on their own.
• Aircover Claims Platform: Facilitates the processing of claims for Aircover insurance products, offering protection to both hosts and guests.

The Difference You Will Make:

There is huge potential for AI in Airbnb's growth intelligence , finding new hosts, building Sales agentic workflows, and how Airbnb scales its reach with AI. You will improve the visibility into policies and their impact on Airbnb business and also to reduce the risk  by improving the intelligence of our product through state of the art machine learning models. This will involve close partnerships and influencing stakeholders across tech, product, and design organizations to identify, scope, and deliver AI features. While you will be leading efforts with XFN stakeholders supported by a team of engineers, you’ll also be working hand in hand with the team to prototype, evaluate, and implement models.

A Typical Day: 

• Design, develop, productionize and operate Machine learning models, including Large-Language-Models, and pipelines at scale, for both batch and real-time use cases.
• Collaborate with machine learning infrastructure engineering teams to evolve how we build reusable and scalable AI/ML solutions for Airbnb products.
• Be the technical lead and owner of significant scope, working through ambiguity, concept validation and implementation of a best-in-class solution.
• Build our platforms--infrastructure, applications and tools using your expertise in distributed systems, large compute clusters and petabyte-scale storage infrastructure.
• Scale distributed applications in a highly-available environment, make architectural trade-offs applying design patterns and disciplined execution.
• Work with cross-functional teams with design, product, data science, and research partners to drive engineering decisions and influence outcomes.

Your Expertise:

• 9+ years of industry experience in applied Machine Learning, inclusive MS or PhD in relevant fields.
• Strong programming (Scala / Python / Java / C++ or equivalent) and data engineering skills.
• Deep familiarity with machine learning development practices, especially for language modeling & NLP applications.
• Prior experience with deploying or supporting production ML systems at large scale - including operational experience on observability, continuous evaluation and improvement, progressive rollouts.
• Familiarity with OSS tools and technologies such as: PyTorch, LlamaFactory, HuggingFace, NVidia Triton / TRTLLM, Ray.
• Passionate about GenAI with a strong grasp of current trends in LLMs and related technologies. Deep understanding of GenAI models and their strengths/weaknesses, and ability to define the criteria for the right models/technologies to use to balance tradeoffs between quality, cost, performance, ease of operation and extensibility.
• Strong communication and cross-team collaboration skills.
• You may currently hold titles such as ML Engineer, Applied Scientist, or Data Scientist.