• Build, train, and ship ML models for identity verification use cases such as biometric matching, liveness / anti-spoofing, identity document processing (OCR/extraction), and fraud detection (team assignment based on experience). • Prepare large, noisy datasets: ingestion, validation, cleaning, deduplication, labeling strategy, and dataset QA to improve model performance and reliability. • Design experiments, evaluation protocols, and success metrics (offline and online), iterate based on measurable business impact (detection rates, fraud losses, false positives). • Develop production-grade training and inference pipelines on AWS with strong reproducibility, monitoring, and cost controls. • Productionize models as resilient services and libraries in Python; collaborate with platform teams on APIs, latency and observability. • Contribute to the transformation of our IDV engine: modernizing legacy components, improving modularity, and raising quality, performance, and maintainability. • Work closely with Product, Customer Success, and Platform Engineering teams to ensure ML solutions meet privacy, compliance, and reliability requirements. • Support other engineers through design reviews, code reviews, and knowledge sharing; help raise the technical bar across the team.

• 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. • Hands-on develop, productionize, and operate Machine Learning 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. • Example projects include: feature platform, model interpretability, hyperparameter optimization, concept drift detection.

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 Relevance and Personalization team at Airbnb is responsible for search and recommendation across the entire Airbnb digital platform. Be a leader in the team working on critical, impactful projects with focus on developing end-to-end ranking algorithms and ecosystems for optimizing multiple critical business objectives. The Difference You Will Make: We build cutting-edge AI technologies across the end-to-end search ranking product stack w.r.t. data pipelines, feature and model innovations, serving and experimentation efficiency, leveraging rich signals from various types of data (structured, sequential, image, text, etc) at Airbnb. We collaborate closely with teams across Airbnb to develop the ranking solutions and support a healthy marketplace for hosts and guests to further Airbnb’s mission of creating a world where people can Belong Anywhere. Some past publications from the team can be found here: https://sites.google.com/view/airbnb-relevance-publications/home A Typical Day: - 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. - Hands-on develop, productionize, and operate Machine Learning 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. - Example projects include: feature platform, model interpretability, hyperparameter optimization, concept drift detection. Your Expertise: - 5+ 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 understanding of Machine Learning best practices (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (eg. neural networks/deep learning, optimization) and domains (eg. natural language processing, computer vision, personalization, search and recommendation, marketplace optimization, anomaly detection) - Experience with 3 or more of these technologies: Tensorflow, PyTorch, Kubernetes, Spark, Airflow (or equivalent), Kafka (or equivalent), data warehouse (eg. Hive) - Industry experience building end-to-end Machine Learning 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. Your Location: 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. Click here for the up-to-date list of excluded states. This list is continuously evolving, so please check back with us if the state you live in is on the exclusion list. If your position is employed by another Airbnb entity, your recruiter will inform you what states you are eligible to work from. Our Commitment To Inclusion & Belonging: Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions. All qualified individuals are encouraged to apply. We strive to also provide a disability inclusive application and interview process. If you are a candidate with a disability and require reasonable accommodation in order to submit an application, please contact us at: reasonableaccommodations@airbnb.com. Please include your full name, the role you’re applying for and the accommodation necessary to assist you with the recruiting process. We ask that you only reach out to us if you are a candidate whose disability prevents you from being able to complete our online application. How We'll Take Care of You: Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits. Pay Range $191,000—$225,000 USD

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role involves supporting a leading AI research lab working on improving how advanced AI systems understand real-world software engineering workflows and open-source development practices. - Design and oversee evaluations for coding tasks across multiple programming languages including Python, Java, C, JavaScript, and TypeScript - Develop test cases to assess system behavior across diverse engineering scenarios - Analyze system performance on real-world software engineering workflows and use cases - Identify strengths, weaknesses, and improvement opportunities in AI-generated engineering outputs - Provide structured feedback to support the development and optimization of advanced AI systems - Collaborate with research teams to refine evaluation methodologies and technical benchmarks Qualifications - A strong GitHub or open-source presence with consistent, high-quality contributions to major repositories - Expertise in one or more languages such as Python, Java, C, JavaScript, or TypeScript - Deep familiarity with widely-used libraries, frameworks, and tooling ecosystems in their language of expertise - Strong understanding of software architecture, performance optimization, and scalable system design - Experience collaborating in distributed and asynchronous engineering teams - Strong analytical thinking and excellent technical communication skills Requirements - Experience working on large-scale open-source frameworks or infrastructure projects (Nice to Have) - Familiarity with CI/CD pipelines, Git workflows, and open-source governance practices (Nice to Have) - Experience contributing to widely adopted developer tools or libraries (Nice to Have) Benefits - Contribute professional expertise to frontier AI research projects - Help improve how AI systems understand real-world software engineering and open-source development workflows - Apply open-source engineering expertise to improve next-generation AI systems - Flexible remote project work with competitive compensation Contract Details - Independent contractor role - Fully remote and flexible scheduling - Part-time engagement - Compensation: $100–$140 per hour - Weekly payments via Stripe or Wise - Projects may extend or adjust depending on scope and performance About the Platform This opportunity is available through a leading AI-driven work platform partnering with top AI labs and enterprises to train frontier models using human expertise.