Machine Learning Engineer
#TeamNextdoor
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 300,000 neighborhoods across 11 countries.
Meet your Future Neighbors
As a Machine Learning Engineer with Nextdoor, Inc. (San Francisco, CA) (100% Telecommuting permitted) you’ll:
- Build Machine Learning (“ML”) infrastructure to enable the team to build, maintain, and iterate on ML products.
- Develop and optimize ML serving infrastructure to provide low latency online ML inference and efficient offline/batch data processing that utilizes ML models.
- Develop and iterate on a ML training platform that can train new models and optimize hyper parameters in a scalable, easy-to-use, and cost-efficient manner.
- Develop and iterate on a feature store that can store data to be used by ML models to train and serve ML models more efficiently.
- Develop underlying data processing infrastructure to support ML platform in collecting data, labeling data and using data to monitor model performance.
- Build infrastructure to train, serve and monitor ML models.
- Build and improve scalable systems to train and optimize ML models.
- Build ranking ML models, and perform data analysis and feature engineering in support of model building.
What You’ll Bring to The House
- Bachelor’s degree, or foreign equivalent, in Statistics, Management Information Systems, Engineering, Computer Science or a closely related quantitative discipline.
- Two (2) years of progressive post-bachelor’s experience as a Data Scientist/Analyst, or closely related position.
- Must have demonstrated experience in the following: Leveraging knowledge of ML to build, train, deploy, and maintain machine learning models that utilize time series data, customer or tabular data, or image/video data; Developing new ML techniques and improving statistical and ML model performance using Python, Structured Query Language (SQL), and Java programming languages; Analyzing datasets and using important features to build ML models; Utilizing A/B Testing Analysis to run and analyze live user-facing experiments to iterate on model quality by measuring impact on business metrics; Automating and building deployment processes and improving productivity using Google Cloud Platform and Continuous Integration/Continuous Delivery pipelines with Jenkins; Tracking different versions of application codes through GIT; Utilizing data processing languages/tools including SPARK and/or Kafka; and Experience with Sci-kit learn, Tensorflow, PyTorch and/or ML frameworks.
Rewards
Compensation, benefits, perks, and recognition programs at Nextdoor come together to create one overall rewards package.
The starting salary for this role is expected to range from $120,000 to $192,000 on an annualized basis, or potentially greater in the event that your 'level' of proficiency exceeds the level expected for the role. Compensation may also vary by geography.
We also expect to award a meaningful equity grant for this role. With equal quarterly vesting, your first vest date would be within the first 3 months of your start date.
Overall, total compensation will vary depending on your relevant skills, experience, and qualifications.
We have you covered! Nextdoor employees can choose between a variety of great health plans. We cover 100% of your personal monthly premium for health, dental, and vision – and 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 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.