Senior Machine Learning Engineer, Airbnb
Trust
$200-235k
Community marketplace for holiday accomodation
Open for applications
Community marketplace for holiday accomodation
1001+ employees
Open for applications
$200-235k
1001+ employees
Company mission
To create a world where anyone can belong anywhere.
Role
Who you are
- 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
- 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
- A Bachelor's, Master's, or PhD in CS/ML or a related field
What the job involves
- Everyone at Airbnb thinks about trust, but our team obsesses over it daily
- At the core of trust is safety, and thus we spend a significant amount of our time and energy keeping the community safe
- The Trust team is responsible for developing the technology that helps protect our community and platform from fraud while also ensuring our hosts, guests, homes, and experiences meet our high standards
- We constantly work to fight against online fraud (such as monetary loss, compromised accounts, spam and scam in messages, fake inventory, etc.) as well as offline fraud (theft, property damage, personal safety, etc.)
- We also work on onboarding and screening of users, and think about complex topics like identity and reputation to ensure that every interaction with Airbnb helps build trust in us and our community
- The Trust Frontier AI team is where new AI technology for Trust gets invented and proven
- We build specialized models for mission-critical trust and safety problems, develop the AI agents and agentic capabilities that automate trust decisions, and create the benchmarks and evaluation harnesses that keep decision quality high as those agents take on more autonomy
- We work on problems before the answer is known — prototyping, experimenting, and iterating with our partner teams until a solution proves itself against real business and top line metrics
- You'll work side-by-side with talented product managers, data scientists, software engineers, fraud intelligence, and operations teams
- Together, you'll design and build ML solutions that have direct, meaningful impact on user trust, business success, and the global Airbnb community
- As a Senior Machine Learning Engineer on the Trust Frontier AI team, you will actively contribute code and ideas that shape the next generation of AI systems protecting millions of Airbnb users
- You'll own and deliver ML projects end-to-end, from framing an ambiguous problem and prototyping a solution, to training and productionizing models, to proving impact on top line metrics with front line teams
- You'll work on abuse behavior detection that spans multiple defenses, on AI agents that make trust decisions autonomously, and on the evaluation and benchmarking work that makes those decisions trustworthy
- Much of this work is early: you will help decide what to build, not only how to build it, and you'll see it through to measurable impact on the platform
- A Typical Day:
- 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
Our take
When Airbnb was originally founded, the product helped customers book air mattresses on people's apartment floors. Since then, Airbnb has had over a billion customers hosted through its platform. With more than 4 million registered hosts worldwide, it has grown into a major player in the hospitality sector.
Airbnb has been successful in disrupting the traditional holiday lettings sector. Before it existed accommodation was limited to hotels and bed and breakfasts, but Airbnb has mainstreamed the idea of renting out spare rooms or even entire homes. It has created a new sector of hosts purchasing properties to rent out on the platform and provides a level of flexibility and a range of offerings not previously possible for travelers.
Perhaps a victim of its own success, by 2022 supply of Airbnb-listed properties began to outstrip demand in some markets, as hosts overestimated the appetite for domestic travel. However, the company is still posting strong financials and has unveiled new tools to support its hosts in a tougher rental market. With a healthy bottom line and a huge user base, Airbnb is very likely to overcome the current market turbulence and continue its growth.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
25% employee growth in 12 months
Company
Funding (last 2 of 10 rounds)
Apr 2020
$500m
GROWTH EQUITY VC
Mar 2017
$447.8m
SERIES F
Total funding: $3.8bn
Company benefits
- Paid volunteer time
- Health food and snacks
- Generous parental and family leave
- Learning and development
- Annual travel and experiences credit
Company HQ
Showplace Square, San Francisco, CA
Leadership
Brian Chesky
(Co-Founder & CEO)Previously an Industrial Designer, studying at Rhode Island School of Design.
Nathan Blecharczyk
(Co-Founder & CSO)Studied Computer Science at Harvard, and had around 4 years of professional Software Engineering experience before founding Airbnb, including interning at Microsoft.
Joe Gebbia
(Co-Founder)Also studied Industrial Design at Rhode Island, and previously founded various design-related startups
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