Join Nextdoor as a Machine Learning Infra Engineer (Core ML) for New Grads 2025 in San Francisco. Build foundational ML infrastructure and make an impact across neighborhoods globally.
About Nextdoor – Empowering Neighborhoods Through AI
At Nextdoor, our mission is to build stronger, kinder neighborhoods by empowering our members to connect, share, and engage with their communities. As a Machine Learning Infra Engineer on the Core ML team, you’ll be part of the team transforming our platform with advanced machine learning that powers personalized experiences and enables real-time decision-making. Join us in making the world a kinder place through innovative AI technologies.
Machine Learning Infra Engineer, Core ML – New Grad 2025 Role at Nextdoor
The Machine Learning Infra Engineer will play a key role in building the foundational ML infrastructure at Nextdoor, enabling the development of data-intensive products. You’ll be working with petabytes of data, helping build robust distributed systems, and collaborating with engineers and data scientists to power the future of machine learning at Nextdoor. This is an excellent opportunity for New Grads 2025 to get started in a fast-paced, impactful AI engineering role.
Key Details of the Machine Learning Infra Engineer Position at Nextdoor
| Company Name | Nextdoor |
|---|---|
| Job Title | Machine Learning Infra Engineer, Core ML – New Grad 2025 |
| Location | San Francisco, California, USA |
| Salary | $150,000+ annually |
| Employment Type | Full-time |
| Compensation | Base salary + equity grant |
Key Responsibilities of the Machine Learning Infra Engineer, Core ML Role
- Build foundational Machine Learning infrastructure that supports data scientists and engineers across the platform.
- Design and implement systems for ML models, including the feature store, real-time serving layer, and offline training system.
- Collaborate with cross-functional teams to create optimal experiences on the Core ML platform, enabling better and faster ML model development.
- Debug, maintain, and optimize highly available distributed systems that manage large-scale data.
What You’ll Bring to the Team as a Machine Learning Infra Engineer at Nextdoor
- Internship in machine learning engineering or related fields (e.g., social networking, e-commerce).
- Strong understanding of ML concepts and applications, such as deep learning, recommender systems, and knowledge graphs.
- Proficiency in programming languages like Python, Golang, or Kotlin.
- Excellent communication and collaboration skills to work with dynamic teams.
- Passion for Nextdoor’s mission to create kinder communities and empower local exchange.
Bonus Points for the Machine Learning Infra Engineer Role at Nextdoor
- Master’s or Ph.D. in Computer Science, Applied Math, Statistics, or a related field.
- Industry experience in applying machine learning at scale.
- Experience with Python, Kubernetes, Kafka, Docker, Spark, SQL, AWS, and the Unix environment.
- Familiarity with machine learning libraries and frameworks like TensorFlow, PyTorch, Xgboost, and Sklearn.
Compensation and Benefits for the Machine Learning Infra Engineer Role
- Base salary: $150,000+ annually, with potential increases based on proficiency.
- Equity grants: Meaningful equity with quarterly vesting.
- Comprehensive benefits: Health plans, wellness stipend, mental health support, and more.
- Generous time off: Flexible paid time off and dedicated volunteer days.
- Work-life balance: Hybrid work environment with 6 office visits per month.
- Diversity & inclusion: Nextdoor values diversity and encourages applicants from all backgrounds.
FAQs About the Machine Learning Infra Engineer Position at Nextdoor
- Where is this role based?
The position is located in San Francisco, California, with a hybrid work environment. - What is the salary range for this position?
The base salary starts at $150,000 annually, with potential increases based on expertise and proficiency. - Is this role open to new graduates?
Yes, this position is specifically for New Grads 2025 with an interest in machine learning engineering.
How Your Profile Fits the Machine Learning Infra Engineer Role
To optimize your resume for this position:
- Highlight any machine learning internships or academic projects related to ML infrastructure or distributed systems.
- Demonstrate your programming skills in Python, Golang, or Kotlin.
- Showcase your understanding of deep learning, recommender systems, and experience working with large-scale data.
How Can You Best Position Yourself for the Machine Learning Infra Engineer Role?
- Tailor your resume to emphasize your experience with ML infrastructure, distributed systems, and machine learning libraries.
- Include any experience with large datasets or real-time decision-making systems.
- Show enthusiasm for Nextdoor’s mission and demonstrate how your skills align with the company’s values and objectives.
Step 1: Understand the Role
This position focuses on building foundational Machine Learning (ML) infrastructure to support ML engineers and data scientists at Nextdoor. The role involves designing, implementing, and integrating systems that handle large-scale data for real-time decisions, including working on the feature store, real-time serving layer, and offline training systems.
Key Responsibilities:
- Build and maintain ML infrastructure to support data scientists and engineers.
- Work with large-scale data and ensure systems are highly available.
- Collaborate with engineers to improve the Core ML platform.
- Develop systems for real-time decision-making for the platform.
Step 2: Assess Required Skills
The role requires proficiency in various technical and soft skills:
Core Skills:
- Machine Learning Concepts:
- Understanding of deep learning, recommender systems, and knowledge graphs.
- Familiarity with ML techniques like supervised learning, unsupervised learning, and deep learning models.
- Programming Languages:
- Strong experience in Python, Golang, or Kotlin.
- Familiarity with SQL, Spark, and AWS.
- Distributed Systems:
- Experience working with large-scale data, debugging, and maintaining distributed systems.
- Familiarity with tools like Kubernetes, Kafka, Docker, and Spark.
- Machine Learning Frameworks:
- Hands-on experience with Xgboost, Scikit-learn, TensorFlow, and PyTorch.
- Collaboration & Communication:
- Ability to work in a fast-paced environment with strong verbal and written communication skills.
- Collaborate with cross-functional teams (data scientists, ML engineers, product teams).
Step 3: Build & Enhance Relevant Experience
Hands-on Projects:
- Machine Learning Applications: Build projects involving recommender systems, deep learning models, or knowledge graphs using libraries like TensorFlow or PyTorch.
- Infrastructure Projects: Create and deploy distributed systems for data-intensive applications, using AWS or Google Cloud, Kubernetes, and Docker.
Contribute to Open Source:
- Contribute to or build open-source ML infrastructure tools or models, such as creating real-time serving systems or working on a distributed feature store.
Step 4: Learn About Nextdoor’s Work and Technology Stack
1. Company Background:
- Nextdoor focuses on personalized experiences for its users through machine learning.
- The company aims to use ML ethically to foster healthy interactions and positive community building.
2. Technologies and Tools:
- Nextdoor uses tools like Kubernetes, Kafka, Docker, and Spark for handling large-scale data and distributed systems.
- Familiarize yourself with the Core ML platform, which powers personalized content, notifications, and recommendations.
Step 5: Prepare for the Interview
1. Technical Interviews:
- Expect questions on distributed systems, including system design and handling petabytes of data.
- Be prepared to discuss your experience with ML frameworks, such as TensorFlow and PyTorch.
- You might be asked to design a machine learning infrastructure system or solve problems related to data processing, real-time serving, or feature storage.
2. Behavioral Interviews:
- Show your passion for Nextdoor’s mission and the role of ML in community building.
- Emphasize your ability to work in a dynamic startup environment and collaborate effectively with teams.
Step 6: Review Compensation and Benefits
Compensation:
- Base Salary: $150,000 (potentially higher based on proficiency).
- Equity: Stock options with quarterly vesting.
Benefits:
- Health plans, OneMedical membership, wellness stipends, and mental health support.
- Flexible paid time off and volunteer days.
Step 7: Final Preparation Tips
- Study Distributed Systems:
- Understand concepts like consistency, scalability, fault tolerance, and high availability.
- Resources: Designing Data-Intensive Applications by Martin Kleppmann.
- Machine Learning for Production:
- Focus on productionizing ML models, handling large datasets, and optimizing real-time serving systems.
- Learn how to scale ML infrastructure using tools like Kubeflow, TensorFlow Serving, or MLflow.
- System Design:
- Practice designing systems that handle large amounts of data, such as building scalable feature stores or real-time model serving systems.
- Review system design interview resources, such as “System Design Interview” by Alex Xu.
