Note: The job is a remote job and is open to candidates in USA. Airbnb is a global platform that connects hosts and guests and works to build trust and safety across its community. The Senior Machine Learning Engineer will develop and productionize machine learning models, AI agents, evaluation systems, and abuse detection solutions to protect users and improve trust decisions. The role owns ML projects end-to-end and partners with product, data science, engineering, fraud intelligence, and operations teams to deliver measurable impact.
Responsibilities
- Frame and prototype ML and agentic solutions for problems that do not yet have an established approach, in partnership with product managers, data scientists, and front line defense teams
- Design, build, and productionize end-to-end Machine Learning pipelines - including feature engineering, model training, evaluation, and deployment - for both batch and real-time use cases
- Build and improve abuse behavior detection that generalizes across defenses
- Design, launch, and iterate on AI agents that automate trust decisions, including orchestration, tool interfaces, and the guardrails that hold quality steady as autonomy increases
- Build benchmarks, evaluation harnesses, and instrumentation that let us measure agentic and model decision quality objectively, and use them to drive real improvements
- Develop specialized models for trust and safety use cases, and use LLMs and AI agents to accelerate how we build models
- Write, review, and ship clean, testable code - whether training a new model, improving an existing pipeline, or optimizing a feature for scalability and reliability
- Work with large-scale structured and unstructured data to continuously improve ML models for Airbnb product, business, and operational use cases
- Partner with front line defense teams to validate solutions through experiments and holdouts, and quantify their impact on business and operational metrics
- Participate in code reviews, design discussions, and cross-team collaborations to contribute to a high-quality ML engineering culture
Skills
- 5-10 years of industry experience in applied Machine Learning, with a track record of building and productionizing models at scale
- 1-2+ years of hands-on experience with LLMs and GenAI technologies, including building with agentic frameworks, orchestration, and evaluation
- Strong programming skills in Python (required) and familiarity with Scala, Java, or equivalent
- Solid understanding of Machine Learning best practices - e.g., training/serving skew minimization, A/B testing, feature engineering, model selection - and algorithms such as gradient boosted trees, neural networks, transformers, and deep learning
- Experience with ML frameworks and tooling such as TensorFlow, PyTorch, or equivalent
- Experience with data engineering and building end-to-end ML pipelines, including both batch and real-time systems
- Experience designing evaluation methodology for ML or LLM systems - benchmarks, ground truth, offline/online metrics, calibration
- Comfort with ambiguity and a bias toward action: you can take a loosely defined problem, scope it, prototype quickly, and drive it to a measurable outcome
- Exposure to architectural patterns of large, high-scale software applications (e.g., well-designed APIs, high-volume data pipelines, efficient algorithms)
- Experience with test-driven development, incremental delivery, and deployment practices
- A Bachelor's, Master's, or PhD in CS/ML or a related field
- This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager
- While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity
- Experience with multimodal models (vision, document, or speech) is a plus
- Exposure to the Trust and Risk domain (e.g., fraud detection, anomaly detection, identity, account integrity) is a plus
Benefits
- Eligible for bonus
- Eligible for equity
- Eligible for Employee Travel Credits
- US - Remote Eligible
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