Senior Machine Learning Engineer, Airbnb
Offline Risk
Community marketplace for holiday accomodation
Job no longer available
Community marketplace for holiday accomodation
1001+ employees
Job no longer available
1001+ employees
Company mission
To create a world where anyone can belong anywhere.
Role
Who you are
- 5+ years of industry experience in applied machine learning, including a MS or PhD in relevant fields
- A Bachelor’s, Master’s, or PhD in CS/ML or related field
- Strong programming skills in Scala, Python, Java, C++, or equivalent languages, and data engineering skills
- Strong understanding of machine learning best practices (e.g., training/serving skew minimization, A/B testing, feature engineering, feature/model selection), algorithms (e.g., neural networks/deep learning, gradient boosted trees, optimization), and domains (e.g., natural language processing, computer vision, personalization and recommendation, anomaly detection)
- Experience with two or more of these technologies: TensorFlow, PyTorch, Kubernetes, Spark, Airflow (or equivalent), Kafka (or equivalent), data warehouses (e.g., Hive)
- Industry experience building end-to-end machine learning infrastructure and/or building and productionizing machine learning models
- Exposure to architectural patterns of large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models)
- Experience with test-driven development, familiarity with A/B testing, incremental delivery, and deployment
- Experience with the Trust and Risk domain is a plus
What the job involves
- Your work on the Offline Risk team will directly impact the safety of Airbnb users worldwide. By building real-time risk detection services and developing advanced machine learning models, you will help us stay ahead of evolving threats. Your contributions will ensure that our platform remains secure and trustworthy, making a tangible difference in the lives of our hosts and guests
- Work with large-scale structured and unstructured data to build and continuously improve cutting-edge machine learning models for Airbnb’s product, business, and operational use cases. Some recent works include:
- Supervised model ensembles that include multiple DNNs and graph-based models
- Contextual Decision engine
- Unsupervised clustering models for natural language understanding
- LLM experimentation with label taxonomy and manual agent automation
- Optimizing ranking algorithms for search
- Collaborate with a wide variety of business functions to predict and prevent physical safety and property damage incidents
- Develop new holistic machine learning model detection strategies by partnering with other teams across the Trust Organization
- Work collaboratively with cross-functional partners, including software engineers, data scientists, product managers, and operations to identify opportunities for business impact, and refine and prioritize requirements for fraud detection and mitigation
- Hands-on develop, productionize, and operate machine learning models and pipelines at scale, including both batch and real-time use cases
- Enhance and extend risk investigation tools to enable efficient decision-making on behaviors that could result in physical safety or property damage incidents
- Create products to deter bad actors and restrict their usage on the platform
- Provide and educate on guest and host safety standards to mitigate vulnerabilities
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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