As a machine learning engineer or scientist, your expertise will be pivotal in developing AI-powered solutions to shape the future of the Airbnb agentic growth platform with cutting-edge AI techniques. You will drive and guide the rest of the engineers to brainstorm, design and develop AI products and features from inception to production.
Collaborate with cross-functional leaders, build resilient systems that operate globally at scale, and help evolve the foundational building blocks behind AI-powered growth systems.
Work with large scale structured and unstructured data; explore, experiment, build and continuously improve Machine Learning models and pipelines for Airbnb product, business and operational use cases.
Work collaboratively with cross-functional partners including product managers, operations and data scientists, to identify opportunities for business impact; understand, refine, and prioritize requirements for machine learning, and drive engineering decisions.
Hands-on develop, productionize, and operate ML/AI 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.
Collaborate actively with engineers to apply ML / AI in their solutions to help validate ideas and guide to the right outcomes.
Partner with ML/AI Engineers in foundations engineering to mentor and develop initiatives that make ML/AI applications a core discipline for non-ML/AI engineers.

Requirements

12+ years of industry experience in applied ML/AI, inclusive MS or PhD in relevant fields
Strong programming (Scala / Python / Java / C++ or equivalent) and data engineering skills
Deep understanding of ML/AI best practices (e.g. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (e.g. neural networks/deep learning, optimization) and domains (e.g. natural language processing, computer vision, personalization, search and recommendation, marketplace optimization, anomaly detection)
Deep understanding of Machine Learning best practices (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection) and algorithms (eg. gradient boosted trees, neural networks/deep learning, optimization)
Experience with technologies such as: Tensorflow, PyTorch, Kubernetes, Airflow (or equivalent), Kafka (or equivalent)
Expertise with architectural patterns of large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models)
Agentic and Automation: Experience with AI technologies in automating processes and developing agentic solutions and frameworks.
Agile Practice for AI Production: Experience with the entire AI product development lifecycle from incubation to production at scale, following agile practices in the Applied AI/ML domain.
Infrastructure Acumen: Experience building robust testing frameworks for agent behavior validation and continuous improvement, and driving architectural requirements on ML infrastructures.

Benefits

This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningartificial intelligencedata engineeringprogrammingfeature engineeringneural networksdeep learningoptimizationA/B testinganomaly detection

Soft Skills

collaborationmentorshipleadershipcommunicationproblem-solvingcross-functional teamworkorganizational skillsbrainstormingguidanceprioritization

Certifications

MS in relevant fieldsPhD in relevant fields