Senior Machine Learning Engineer, Trust
About this role
Engineers on the Trust team spend their days collaborating with cross-functional partners to scope solutions, investigate emerging fraud patterns, and productionize machine learning pipelines for batch and real-time use cases. Working side-by-side with product managers, data scientists, software engineers, and operations teams, this group designs and builds ML systems that protect the global community from online and offline fraud. The team exists to safeguard user interactions and maintain platform standards by tackling complex challenges like account takeovers, fake accounts, payment fraud, and bot detection.
A successful candidate brings extensive applied machine learning experience and a collaborative mindset to deliver end-to-end models at scale. Individuals who thrive here enjoy diving into large structured and unstructured datasets, writing clean and testable code, and adapting systems to an evolving landscape of malicious actors. This senior position offers a chance to directly influence user trust and safety while shaping the engineering culture through design discussions and code reviews.
What you will do
- Collaborate with product managers, data scientists, software engineers, and operations teams to scope machine learning solutions and refine requirements for trust models.
- Design, build, and productionize end-to-end machine learning pipelines encompassing feature engineering, model training, evaluation, and deployment for batch and real-time use cases.
- Investigate emerging fraud patterns and threat signals alongside teammates to develop machine-learning-based detections and tools.
- Write, review, and ship clean, testable code to train new models, improve existing pipelines, and optimize features for scalability and reliability.
- Work with large-scale structured and unstructured data to continuously improve machine learning models for product, business, and operational needs.
- Participate in code reviews, design discussions, and cross-team collaborations to contribute to a high-quality machine learning engineering culture.
- Partner with trust defense and platform teams to adapt models and systems to an evolving landscape of fraud attacks.
What you need
- Requires five or more years of industry experience in applied machine learning with a track record of building and productionizing models at scale.
- Demonstrates strong programming skills in Python alongside familiarity with Scala, Java, or equivalent languages.
- Possesses a solid understanding of machine learning best practices such as training and serving skew minimization, A/B testing, feature engineering, and model selection.
- Brings experience with machine learning frameworks and tooling like TensorFlow, PyTorch, or equivalent technologies.
- Shows proven experience with data engineering and building end-to-end machine learning pipelines for batch and real-time systems.
- Requires familiarity with architectural patterns of large, high-scale software applications including well-designed APIs and high-volume data pipelines.
- Holds a Bachelor's, Master's, or PhD in Computer Science, Machine Learning, or a related field.
Nice to have
- Exposure to the Trust and Risk domain such as fraud detection, anomaly detection, identity, and account integrity
Skills and tech stack
Questions candidates ask
What is the remote work policy for this role?
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.
Are there geographic restrictions for remote employees?
You must live in a state where Airbnb, Inc. has a registered entity. Candidates must check the up-to-date list of excluded states as this list is continuously evolving.
What kind of projects will I work on?
You will tackle real-world challenges such as account takeover, fake accounts, payment fraud, and bot detection. Your work will help reduce risks posed by bad actors while ensuring the platform remains seamless and welcoming.
Who will I be working with on a regular basis?
You will work side-by-side with talented product managers, data scientists, software engineers, fraud intelligence, and operations teams. Together, you will design and build ML solutions that have direct, meaningful impact on user trust.