Senior Staff Machine Learning Engineer, Trust

Responsibilities

• Define and execute on the long-term ML technical vision and strategy for the Trust organization, identifying key investments, architecting scalable solutions, and championing best practices that advance the state-of-the-art in production ML systems.
• Serve as a technical leader and mentor to other ML and software engineers across the organization, providing guidance on complex architectural and modeling challenges, and raising the overall technical bar.
• Drive and deliver large-scale, multi-quarter ML initiatives that span multiple teams, influencing roadmaps and ensuring alignment between platform and product.
• 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.
• Work closely with other trust defense and platform teams to tackle the changing landscape of fraud attacks.
• Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases.
• Examples include: Anomaly detection models, ML models for continuous risk evaluation, Multimodality and Agentic AI.

Requirements

• 12+ years of industry experience in applied Machine Learning.
• 2-3+ years working with LLMs and novel GenAI technologies.
• Proficiency and proven experience on Agentic AI (frameworks, orchestration, architecture and productionization).
• A Bachelor’s, Master’s or PhD in CS/ML or related field.
• Strong programming (Scala / Python / Java/ C++ or equivalent) and data engineering skills.
• 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. genAI, Agentic AI, natural language processing, computer vision, personalization and recommendation, anomaly detection).
• Experience with these technologies: AgenticAI, Tensorflow, PyTorch, Kubernetes.
• Industry experience building end-to-end Machine Learning and Agentic infrastructure and/or building and productionizing Machine Learning models.
• 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.

Nice-to-Haves

• Experience with the Trust and Risk domain.