Staff Machine Learning Engineer, Airbnb
Trust
$204-255k
More information about location
Community marketplace for holiday accomodation
Posted today
Community marketplace for holiday accomodation
1001+ employees
Posted today
$204-255k
More information about location
1001+ employees
Company mission
To create a world where anyone can belong anywhere.
Role
Who you are
- 9+ years of industry experience in applied Machine Learning, inclusive MS or PhD in relevant fields
- A Bachelor’s, Master’s or PhD in CS/ML or related field
- 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. gradient boosted trees, neural networks/deep learning, optimization) and domains (eg. natural language processing, computer vision, personalization and recommendation, 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 the Trust and Risk domain is a plus
What the job involves
- As a Staff Machine Learning Engineer on the Listing Integrity team, you will be working with data scientists, designers, product managers, and customer service operations to innovate new ways we can stop bad actors in the ever evolving fake listing creation fraud and financial losses associated with it by collaborating across team boundaries
- On this team, you must have the curiosity to dig deep into various end to end systems in order to understand how and where the fraud occurs. Your curiosity will be rewarded with finding projects that have outsized impacts on decreasing fraud losses and protecting Airbnb users from bad experiences while on vacation
- 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
- Working together with a wide variety of business functions to stop fraud attacks in real time
- Collaborate with Data Scientists and other Machine Learning Engineers across trust to come up with the best modeling strategy and approach to defending against fake listings
- 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 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
- Examples include: ML models to detect Fake Listing creation attempts
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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