Staff Machine Learning Engineer, Airbnb
Trust Screenings
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
- 8+ 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 test driven development, familiar with A/B testing, incremental delivery and deployment
- Experience with the Trust and Risk domain is a plus
What the job involves
- As a Staff Machine Learning Engineer on the Trust Screenings team, you will be working with product managers, data scientists, designers, and customer service operations to innovate new ways to predict the physical safety and property damage incidents on the platform
- Problems will be so vague and ambiguous hence you must have the curiosity to dig deep into various patterns/behaviors of the users to understand how and where these life safety incidents may occur
- Although you will be one of our technical leaders on the team, all individual contributors at Airbnb are Software Engineers which means we expect you to be hands on and contribute code
- 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 physical safety and property damage incidents in real time
- Creating new holistic machine learning model detection strategies by collaborating with other trust and safety prevention teams around the Trust Organization
- 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
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
Few candidates hear
back within 2 weeks
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 values
- Champion the Mission: We’re united with our community to create a world where anyone can belong anywhere
- Be a Host: We're caring, open, and encouraging to everyone we work with
- Embrace the Adventure: We’re driven by curiosity, optimism, and the belief that every person can grow
- Be a Cereal Entrepreneur: We’re determined and creative in transforming our bold ambitions into reality
Company HQ
Showplace Square, San Francisco, CA
Articles
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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