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Senior Machine Learning Engineer

Nextdoor
Full-time
On-site
San Francisco, United States
$206,315 - $230,000 USD yearly

#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 335,000 neighborhoods across 11 countries.


Meet Your Future Neighbors


As a Senior Machine Learning Engineer with Nextdoor, Inc. (San Francisco, CA) (100% Telecommuting permitted) you’ll:  



  • Lead Machine Learning (ML) development projects end-to-end by designing a solution, breaking it down into tasks, and working with peers to get the project completed

  • Oversee analysis of datasets and usage of important features to build low-latency models

  • Manage relationships with team members and build culture, in-person Company events, trainings, off-sites, and other team building exercises

  • Build foundational patterns and provide mentorship / guidance to Junior ML Engineers


What You’ll Bring to The House



  • Master’s degree, or foreign equivalent, in Computer Science, Applied Math, Statistics, or a closely related quantitative discipline

  • Two (2) years of experience in the position offered, as a Machine Learning Engineer or closely related position

  • Demonstrated experience must include: Working with software engineering standard methodologies including design patterns, peer code reviews, unit testing, functional testing and performance; Leveraging ML to build, deploy, and maintain models utilizing time series data, customer or tabular data, or image/video data; Developing ML techniques and improving statistical and ML model performance using Python, Structured Query Language (SQL) and Java programming languages; Utilizing Generative AI and Large Language Models to construct scalable ML solutions and leverage data from various datasets to extend the functionalities of features for user enhance experience; Building data pipelines to ensure stability of data used in ML production models; Analyzing datasets and using important features to build ML models; Utilizing Natural Language Processing to build word embedding for document clustering and text classification; Utilizing ML model frameworks including Sci-kit learn, Tensorflow, and/or PyTorch; and Utilizing data processing languages/tools including SPARK and/or Kafka..     


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 $206,315 - $230,000/year on an annualized basis. 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 neighbors we seek to 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.