Engineering Manager - Machine Learning Infrastructure at Nextdoor
San Francisco, CA, US
Nextdoor is where neighbors turn for trusted connections and the exchange of helpful information, goods, and services. Nextdoor’s purpose is to cultivate a kinder world where everyone has a neighborhood they can rely on.
Building connections in the real world is a universal human need. That truth, and the reality that neighborhoods are one of the most important and useful communities in our lives have been guiding principles for Nextdoor. Today, neighbors rely on Nextdoor in neighborhoods around the world in the United States, the United Kingdom, Germany, France, the Netherlands, Italy, Spain, Sweden, Denmark, Australia and Canada, with many more to come.
Meet your Future Neighbors
At Nextdoor, the machine learning team is starting to touch every area of our product. Notifications, feed, search, ads, and our trust/moderation systems all have an element of ML on the roadmap. A critical charter for the team is to build world-class ML infrastructure to enable us to build, maintain, and iterate on ML products quickly. Our team is scrappy, focused, and dedicated to ensuring that machine learning helps us realize our purpose: to cultivate a kinder world where everyone as a neighborhood they can rely on.
The Impact You’ll Make
Within the ML organization, you will be leading our Core ML team, which is charged with building, maintaining, and supporting our ML Infrastructure. This includes the training and serving platforms, the corresponding data pipelines, and the feature store (online and offline). As the ML and Data Science teams grow, you will also take on the responsibility of getting feedback from developers to inform the roadmap that could make ML development more efficient at Nextdoor. Some areas of responsibility include:
Leading the execution of a prioritized roadmap to increase the productivity of the ML and Data Science teams by building world-class infrastructure
Leading the development and maintenance of a centralized “feature store” where we will house Nextdoor data, both online and offline
Managing and working with stakeholders to provide input into the roadmap for the Core ML team
Working along-side the team to execute and build infrastructure in a hands-on manner
Recruiting, building, coaching and mentoring engineers to reinforce engineering excellence
Promoting a culture of feedback and transparent communication
What You’ll Bring to The House
1-3 years of experience leading the technical direction and execution of infrastructure related to machine learning
4-8+ years of relevant engineering experience working on infrastructure related to developing, serving and maintaining ML models (ML Infrastructure) and/or managing infrastructure to house model features (i.e. feature stores)
A desire to work along-side the team in a hands-on manner to execute on the team charter
Experience collaborating with teams across an organization, prioritizing the work that is requested from external teams, and keeping the team focused on delivery with many pressing priorities
Experience being a collaborative, thoughtful and strategic leader, who is comfortable with open communication and giving/receiving constructive feedback respectfully.
Successful track record of building ML Infrastructure from inception to scale with the goal of increasing developer productivity
Experience utilizing technology such as Kafka, Protobuf, Flink, Spark to build feature stores
Experience serving of models in production in frameworks such as Tensorflow, Sci-kit Learn, Pytorch, etc. with low latency
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.