Posted:
3/6/2025, 4:42:22 AM
Location(s):
San Francisco, California, United States ⋅ California, United States
Experience Level(s):
Junior ⋅ Mid Level ⋅ Senior
Field(s):
AI & Machine Learning ⋅ DevOps & Infrastructure ⋅ Software Engineering
Workplace Type:
Hybrid
Nextdoor (NYSE: KIND) is the essential neighborhood network. Neighbors, public agencies, and businesses use Nextdoor to connect around local information that matters in more than 340,000 neighborhoods across 11 countries. Nextdoor builds innovative technology to foster local community, share important news, and create neighborhood connections at scale. Download the app and join the neighborhood at nextdoor.com.
At Nextdoor, machine learning is one of the most important teams we are growing. Machine learning is starting to transform our product through personalization, driving major impact across different parts of our platform including our newsfeed, our notifications, and our ads relevance. 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 engineers 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 model, blending an in office presence and work from home experience for our valued employees.
You will be part of a scrappy and impactful team building data-intensive products, working with data and features. You will help build the foundational Machine Learning (ML) infrastructure that ML engineers will use for years to come as we ramp up our effort to introduce machine learning into our platform. You should be comfortable with petabytes of data, writing crisp design documentation, and building, debugging, and maintaining highly available distributed systems. The Machine Learning platform that you build will empower developers throughout Nextdoor to build better ML products more quickly than ever before.
Your responsibilities will include:
Compensation, benefits, perks, and recognition programs at Nextdoor come together to create our total rewards package. Compensation will vary depending on your relevant skills, experience, and qualifications. Compensation may also vary by geography.
The starting salary for this role is expected to range from $205,000 to $336,000 on an annualized basis, or potentially greater in the event that your 'level' of proficiency exceeds the level expected for the role.
We expect to award a meaningful equity grant for this role. With equal quarterly vesting, your first vest date will take place within 3 months of your start date.
When it comes to benefits, we have you covered! Nextdoor employees can choose between a variety of health plans, including a 100% covered employee only plan option, and we also provide a OneMedical membership for concierge care.
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 neighbors we serve. We encourage everyone interested in our mission 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.
#LI-Hybrid
Website: http://nextdoor.com/
Headquarter Location: San Francisco, California, United States
Employee Count: 501-1000
Year Founded: 2011
IPO Status: Public
Last Funding Type: Post-IPO Equity
Industries: CivicTech ⋅ Communities ⋅ Internet ⋅ Social Media ⋅ Mobile Apps