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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer I // II - **Company:** Airbnb - **Location:** San Jose, CA, United States (Remote available) - **Experience:** Expert - **Salary:** $244,000.0 - $305,000.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Airflow, Software Applications, Architectural Patterns, Artificial Neural Networks, Computer Vision, C++ (Programming Language), Computer Programming, Information Engineering, Push Technology, Python (Programming Language), Machine Learning, Natural Language Processing, Software Product Management, Tensorflow, Azure Machine Learning, Unstructured Data, Feature Engineering, Pytorch, Multi-Agent Systems, Deep Learning, Kubernetes, Apache Kafka, Machine Learning Operations, Virtual Agents, Data Pipelines - **Published:** August 29, 2026 - **Apply:** https://www.workingnomads.com/job/go/1819146/ ## About the Role * 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 a 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 ## Description * 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. * We're seeking a Senior Staff Engineer who thrives at the intersection of technical depth, architectural thinking, and mentorship. * You'll 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. Some example projects you will work on: * AI-Powered Content Generation - Developing agentic capabilities to autonomously create personalized emails, push notifications, Ad copy, and creatives. This significantly scales marketing efforts by enabling more campaigns, greater variant testing, and faster iteration cycles. * ML/AI Orchestration for Decisioning - Utilizing AI to determine the optimal audience, message, channel, and timing for communications. This shifts marketing decision-making to model-driven intelligence, enhancing relevance and minimizing message fatigue. The direct impact is an uplift in engagement rates, conversion, and ultimately, bookings. * Proactive Marketing Analyst Agent - Designing an AI agent that autonomously identifies new marketing opportunities and converts them into executable campaigns. It leverages world knowledge, proprietary Airbnb intelligence, and deep customer profiles. A crucial performance-based feedback loop ensures the system continuously learns from campaign outcomes to refine and improve future recommendations. A Typical Day: * 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. ## Related Videos - [Navigating the AI Revolution in Software Development](https://www.wearedevelopers.com/videos/1266-navigating-the-ai-revolution-in-software-development) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Machine Learning for Software Developers (and Knitters)](https://www.wearedevelopers.com/videos/154-machine-learning-for-software-developers-and-knitters) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)