Nextdoor is where you connect to the neighborhoods that matter to you so you can belong. Our purpose is to cultivate a kinder world where everyone has a neighborhood they can rely on.
Neighbors around the world turn to Nextdoor daily to receive trusted information, give and get help, get things done, and build real-world connections with those nearby — neighbors, businesses, and public services. Today, neighbors rely on Nextdoor in more than 305,000 neighborhoods across 11 countries.
Meet Your Future Neighbors
At Nextdoor, Machine Learning is one of the most important teams we are growing. Machine learning is transforming our product through personalization, and driving major impact across different parts of our platform including newsfeed, notifications, ads relevance, connections, search, and trust. Our machine learning team is lean but hungry to drive even more impact and make Nextdoor the neighborhood hub for local exchange. We believe that ML will be an integral part of making Nextdoor valuable to our members. We also believe that ML should be ethical and encourage healthy habits and interaction, not addictive behavior. We are looking for great machine learning interns who believe in the power of the local community to empower our members to make their communities great places to live.
At Nextdoor, we offer a warm and inclusive work environment that embraces a hybrid employment experience, providing a flexible experience for our valued employees.
The Impact You’ll Make
You will be part of an avid and impactful team building data-intensive products, working with data and features, building machine learning models, and sharing insights around data and experiments. You will be working closely with the product team and the Data Science team on a daily basis. Finally, you will help build the foundational patterns that ML engineers will use for years to come as we introduce more and more machine learning into our platform.
Your responsibilities will include:
- Collect and gather datasets to build machine learning models that make real-time decisions for the Nextdoor platform
- Deploy ML models into production environments and integrate them into the product
- Run and analyze live user-facing experiments to iterate on model quality by measuring impact on business metrics
- Participate in in-person Nextdoor events, trainings, off-sites, volunteer days, and other team building exercises
- Build in-person relationships with team members and contribute to the KIND culture that Nextdoor values
We have intern positions across multiple tracks including ML infra, Feed, Notifications, Ads, Network Growth, and Knowledge Graph. Our paid internships are typically 12 weeks, based out of our offices in San Francisco or New York City.
What You’ll Bring to the Team
- Currently pursuing a Master's degree or PhD in Computer Science, Applied Math, Statistics, or a related field
- Deep understanding of machine learning concepts (e.g. deep learning) and applications (e.g. recommender systems, knowledge graph)
- Strong programming skills in Python, Java, or Scala
- Effective communication and collaboration skills
- Ability to be flexible and adaptable in a fast-paced startup environment
- Desire to learn about new technologies and systems
- Passion for the Nextdoor’s mission and purpose
- Internship in machine learning engineering in a related field (e.g. social networking, e-commerce)
At Nextdoor, we empower our employees to build stronger local communities. To create a platform where all feel welcome, we want our workforce to reflect the diversity of the customers we seek to serve. We encourage everyone interested in our purpose to apply. We do not discriminate on the basis of race, gender, religion, sexual orientation, age, or any other trait that unfairly targets a group of people. In accordance with the San Francisco Fair Chance Ordinance, we always consider qualified applicants with arrest and conviction records.
For information about our collection and use of applicants’ personal information, please see Nextdoor's Personnel Privacy Notice, found here.