Be ready to go over:

• Code Refactoring – Identifying code smells, reducing complexity, and improving the modularity of existing scripts.
• Object-Oriented Design – Applying appropriate design patterns to make ML pipelines extensible.
• Algorithmic Foundations – Implementing fundamental algorithms (like sorting or searching) and explaining their computational complexity.

Example scenarios:

• Refactoring a single 300-line script that trains a model into a structured, object-oriented pipeline with separate modules for data loading, preprocessing, training, and evaluation.
• Implementing a custom search algorithm to find matching user segments based on boolean criteria.

As a Machine Learning Engineer at Klaviyo, your day-to-day responsibilities will span the entire lifecycle of machine learning systems, from raw data ingestion to production model monitoring.

• Design and Build Scalable ML Pipelines – You will write robust, distributed data pipelines using Apache Spark and Python to preprocess massive volumes of customer event data, ensuring high data quality and low latency.
• Deploy and Maintain Production Models – You will containerize and deploy predictive models into production environments, exposing them via high-performance APIs that can handle millions of requests per minute.
• Collaborate Across Teams – You will work closely with Data Scientists to transition experimental models into production-ready software, and partner with Product Managers to integrate ML capabilities into the core Klaviyo platform.
• Optimize System Performance – You will continuously monitor, profile, and refactor existing codebases and database queries to improve execution speed, reduce infrastructure costs, and ensure system reliability.
• Implement Robust Monitoring – You will build alerting and monitoring systems to track model performance, input data distributions, and prediction latency, ensuring quick detection of model degradation or system failures.

To be competitive for the Machine Learning Engineer position at Klaviyo, you must demonstrate a strong technical foundation along with practical experience in building production software.

• Must-Have Technical Skills

  • Strong proficiency in Python and its scientific stack (such as Pandas, NumPy, and Scikit-Learn).
  • Deep understanding of SQL and relational database design.
  • Hands-on experience with distributed computing frameworks, specifically Apache Spark.
  • Solid grasp of software engineering fundamentals, including object-oriented programming, data structures, and algorithms.
  • Practical knowledge of probability, statistics, and core machine learning concepts.

• Must-Have Experience

  • 3+ years of experience working as a Machine Learning Engineer or Software Engineer in a production environment.
  • Proven track record of deploying machine learning models to production and managing them at scale.
  • Experience writing unit tests, integration tests, and participating in rigorous code reviews.

• Nice-to-Have Qualifications

  • Experience working with cloud platforms (AWS, GCP) and containerization tools (Docker, Kubernetes).
  • Familiarity with MLOps tools for model tracking, feature stores, and pipeline orchestration.
  • Experience dealing with high-throughput, real-time streaming data (such as Kafka or Kinesis).

Q: Is there traditional Leetcode-style coding in the Klaviyo interview process? A: While you may be asked to implement basic algorithms (like sorting) or solve simple algorithmic problems, Klaviyo relies less on abstract, hyper-optimized Leetcode puzzles. Instead, they focus heavily on practical programming tasks, such as data manipulation, probability simulations, and code refactoring.

Q: What is the take-home data challenge like? A: The take-home challenge is typically a Jupyter Notebook exercise that involves basic data analysis, SQL queries, pandas data manipulation, and a short paragraph explaining your favorite technical tools. It is designed to evaluate your practical, day-to-day data engineering skills and is usually estimated to take 1 to 2 hours.

Q: How technical are the conversations with the VP of Data Science or Hiring Managers? A: These conversations are highly technical but conversational. Expect to walk through your resume, discuss past projects in deep detail, explain the architecture of systems you have built, and potentially participate in a collaborative code review or refactoring discussion.

Q: What is the balance between software engineering and data science in this role? A: The role is heavily weighted toward software engineering. While you must understand the mathematical foundations of the models you deploy, your primary responsibility is building the scalable pipelines, infrastructure, and APIs that allow those models to run reliably in production at scale.

To maximize your chances of success during the Klaviyo interview loop, keep these practical tips in mind:

• Treat the Take-Home Seriously: Even if the take-home assessment seems simple, do not rush through it. Ensure your code is clean, well-commented, and structurally sound. This exercise sets the first impression of your engineering standards.
• Communicate During Simulations: When coding a probability simulation, talk through your approach. Explain how you are translating the mathematical problem into a programmatic loop, and discuss any trade-offs between precision and execution speed.
• Focus on Clean Code Over Speed: During refactoring and coding rounds, prioritize readability and modularity. Use descriptive variable names, write helper functions where appropriate, and explain how you would write tests for your code.
• Be Ready to Discuss Scale: Whenever you propose a system design or a data pipeline, proactively address how it will scale. Mention how you would handle data partitioning, caching, and database indexing to maintain low latency.

The Machine Learning Engineer role at Klaviyo represents a unique opportunity to build high-throughput, intelligent systems that directly drive business value for hundreds of thousands of brands. By focusing your preparation on clean code, practical data manipulation, probability simulations, and distributed systems like Apache Spark, you will position yourself to stand out in the interview process.

Approach each round as a collaborative engineering discussion rather than a rigid test. Klaviyo's engineering team values candidates who are thoughtful, communicative, and passionate about software craftsmanship. With a structured preparation plan and a deep focus on production-grade engineering practices, you can confidently navigate the loop and showcase your ability to scale ML at Klaviyo.

The salary data module above provides a representative view of compensation for this position, including base salary and equity components. When evaluating an offer or discussing compensation with recruiters, keep in mind that Klaviyo values high-performing engineering talent and structures its packages competitively to reflect the scale and impact of the role. Use this data to benchmark your expectations based on your experience level and location. To explore further interview insights, real candidate experiences, and additional preparation resources, visit Dataford.