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Practising for the listing Software Engineer II - Recommendations. Its requirements are filled in below. When you have an answer you like, paste it with your CV in Career Mirror to see which of the job's skills it shows.

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Software Engineer II - Recommendations
At Klaviyo, we value the unique backgrounds, experiences and perspectives each Klaviyo (we call ourselves Klaviyos) brings to our workplace each and every day. We believe everyone deserves a fair shot at success and appreciate the experiences each person brings beyond the traditional job requirements. If you’re a close but not exact match with the description, we hope you’ll still consider applying. Want to learn more about life at Klaviyo? Visit klaviyo.com/careers to see how we empower creators to own their own destiny.

Software Engineer II - Recommendations

(Boston, MA onsite 5x a week)

Why you should join the Recommendations Platform Team

The Recommendations Platform Team is responsible for developing and deploying machine learning-based recommendation systems at scale, and building out the foundation for new use cases for technologies such as embedding-based similarity search to power agentic workflows. We are evolving to act as a layer of product intelligence, making sense of customer data in order to personalize messages with the right item at the right time across multiple channels. Our systems span large-scale data pipelines and querying workflows, batch training and inference, and low-latency online retrieval and ranking systems. We are also building the experimentation, tracking, and measurement capabilities needed to evaluate recommendation quality and business impact over time.

How you will make a difference

• Contribute to the architecture and evolution of backend services that power product recommendations across Klaviyo experiences (email, SMS, KAgent, onsite, etc.), meeting standards for reliability, performance, and clear APIs.

• Contribute to and maintain robust, large-scale data processing pipelines (e.g., using Apache Spark or similar frameworks) that transform raw events and catalog data into high-quality features and inputs for recommendation models, ensuring data quality and lineage.

• Collaborate closely with ML engineers and product stakeholders to productionize recommendation models —defining high-level interfaces, feature contracts, and deployment patterns for batch and/or real-time inference systems.

• Contribute to the development of the vector database that powers recommendation, semantic search, and agentic use cases.

• Ensure data and service observability (metrics, logging, tracing, dashboards) to facilitate recommendations that are correct, explainable, fast, and highly available for all customers.

• Work with Product to break down projects into clear milestones, balancing the need for rapid experimentation with technical soundness and long-term maintainability.

• Lead data-driven decision making and A/B testing efforts —ensuring recommendation systems are instrumented with the right metrics, and independently interpreting results to guide future product and engineering iterations.

• Participate in on-call and incident response for the systems you own, driving major post-incident follow-ups that substantially improve the resilience and operability of our recommendation stack.

• Integrate AI into your and the team’s development workflow from the ground up —for example, using AI to accelerate development, automate complex tests, or build smarter monitoring and debugging tools.

• Share knowledge, mentor junior engineers, and define best practices on working with large-scale data frameworks, distributed systems, and integrating ML into production systems.

Who you are

• 2+ years of professional software engineering experience with a focus on backend and distributed systems at scale; you have a proven track record working on production services and optimizing for latency, reliability, and operability as well as business requirements.

• Proficient in Python and open to working in other languages

• Comfortable with cloud-native architectures (AWS preferred) and container orchestration (e.g., Kubernetes); you manage infrastructure and CI/CD pipelines as a core part of your development process.

• Experience in data-driven decision making and A/B testing —you can define (or are interested in learning how to) how to instrument experiments, read and interpret results, and ensure learnings are folded back into system design.

• Comfortable designing and querying data models in relational, analytical, and NoSQL datastores (e.g., Postgres, MySQL, data warehouses, Redis, vector databases).

• Feel at home with modern DevOps practices (CI/CD, monitoring, alerting) and how to apply them to architect large-scale data and recommendation systems.

• Track record of owning features end-to-end —from initial technical design and implementation through rollout, monitoring, and sustained iteration.

• Excellent technical collaborator and communicator: you can clearly articulate complex technical trade-offs to both technical peers and non-technical partners, and you work effectively to drive alignment across ML Engineers, Software Engineers, PMs, and other teams.

• You are a self-starter who has actively experimented with AI in work or personal projects and are excited to responsibly explore and define new AI tools and workflows to enhance team productivity and system intelligence.

Nice to have

• Previous experience working on product recommendation systems or adjacent ML-powered features (ranking, personalization, search, or similar).

• Experience with big data frameworks such as Apache Spark (or similar technologies like Flink, Beam, etc.) for architecting and building complex batch or streaming pipelines.

• Experience in AI/ML systems and products, such as integrating models into production systems, building features powered by ML, or contributing to the ML infrastructure.

• Experience training and iterating on machine learning models (e.g., for ranking, prediction, or personalization).

• Experience with ML and distributed compute frameworks such as Ray or similar tools.

• Experience partnering with data science or ML teams to productionize models (designing feature stores, ensuring offline/online parity, advanced model deployment and monitoring).

• Background in e-commerce, marketing tech, or consumer personalization products.

Massachusetts Applicants:
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

Our salary range reflects the cost of labor across various U.S. geographic markets. The range displayed below reflects the minimum and maximum target salaries for the position across all our US locations. The base salary offered for this position is determined by several factors, including the applicant’s job-related skills, relevant experience, education or training, and work location.

In addition to base salary, our total compensation package may include participation in the company’s annual cash bonus plan, variable compensation (OTE) for sales and customer success roles, equity, sign-on payments, and a comprehensive range of health, welfare, and wellbeing benefits based on eligibility.

Your recruiter can provide more details about the specific salary/OTE range for your preferred location during the hiring process.

Base Pay Range For US Locations:
$116,000 — $174,000 USD

This role may require up to 10% travel for purposes such as new hire onboarding, client or partner work if applicable, team meetings, and industry events. Travel is coordinated in advance.

Get to Know Klaviyo

We’re Klaviyo (pronounced clay-vee-oh). We empower creators to own their destiny by making first-party data accessible and actionable like never before. We see limitless potential for the technology we’re developing to nurture personalized experiences in ecommerce and beyond. To reach our goals, we need our own crew of remarkable creators—ambitious and collaborative teammates who stay focused on our north star: delighting our customers. If you’re ready to do the best work of your career, where you’ll be welcomed as your whole self from day one and supported with generous benefits, we hope you’ll join us.

AI fluency at Klaviyo includes responsible use of AI (including privacy, security, bias awareness, and human-in-the-loop). We provide accommodations as needed.

By participating in Klaviyo’s interview process, you acknowledge that you have read, understood, and will adhere to our Guidelines for using AI in the Klaviyo interview Process . For more information about how we process your personal data, see our Job Applicant Privacy Notice .

Klaviyo is committed to a policy of equal opportunity and non-discrimination. We do not discriminate on the basis of race, ethnicity, citizenship, national origin, color, religion or religious creed, age, sex (including pregnancy), gender identity, sexual orientation, physical or mental disability, veteran or active military status, marital status, criminal record, genetics, retaliation, sexual harassment or any other characteristic protected by applicable law.

IMPORTANT NOTICE: Our company takes the security and privacy of job applicants very seriously. We will never ask for payment, bank details, or personal financial information as part of the application process. All our legitimate job postings can be found on our official career site. Please be cautious of job offers that come from non-company email addresses (@klaviyo.com), instant messaging platforms, or unsolicited calls.

By clicking "Submit Application" you consent to Klaviyo processing your Personal Data in accordance with our Job Applicant Privacy Notice. If you do not wish for Klaviyo to process your Personal Data, please do not submit an application. You can find our Job Applicant Privacy Notice here and here (FR).

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Requirements found

Training (nice) Python Sql Marketing (nice) Sales (nice) Onboarding

What this posting asks for

Level: Mid level — The posting asks for 2+ years of experience.

Main responsibilities

Contribute to the architecture and evolution of backend services that power product recommendations across Klaviyo experiences (email, SMS, KAgent, onsite, e…
Contribute to and maintain robust, large-scale data processing pipelines (e.g., using Apache Spark or similar frameworks) that transform raw events and catal…
Collaborate closely with ML engineers and product stakeholders to productionize recommendation models —defining high-level interfaces, feature contracts, and…
Contribute to the development of the vector database that powers recommendation, semantic search, and agentic use cases.
Ensure data and service observability (metrics, logging, tracing, dashboards) to facilitate recommendations that are correct, explainable, fast, and highly a…
Work with Product to break down projects into clear milestones, balancing the need for rapid experimentation with technical soundness and long-term maintaina…
Lead data-driven decision making and A/B testing efforts —ensuring recommendation systems are instrumented with the right metrics, and independently interpre…
Participate in on-call and incident response for the systems you own, driving major post-incident follow-ups that substantially improve the resilience and op…
Track record of owning features end-to-end —from initial technical design and implementation through rollout, monitoring, and sustained iteration.

Competencies an interviewer will look for

Technical skill Core
In the posting: “Software Engineer II - Recommendations” · “Contribute to the architecture and evolution of backend services that power product recommendations across Klaviyo experiences (email, SMS, KAgent, onsite, e…” · “Lead data-driven decision making and A/B testing efforts —ensuring recommendation systems are instrumented with the right metrics, and independently interpre…”
Show it by: Names the tools, languages or systems used and how; Explains a technical choice and its trade-off.
Teamwork and collaboration Core
In the posting: “Collaborate closely with ML engineers and product stakeholders to productionize recommendation models —defining high-level interfaces, feature contracts, and…” · “Excellent technical collaborator and communicator: you can clearly articulate complex technical trade-offs to both technical peers and non-technical partners…” · “To reach our goals, we need our own crew of remarkable creators—ambitious and collaborative teammates who stay focused on our north star: delighting our cust…”
Show it by: Says what they did inside a team, not only what 'we' did; Shows how a disagreement was handled.
Ownership and accountability Core
In the posting: “Lead data-driven decision making and A/B testing efforts —ensuring recommendation systems are instrumented with the right metrics, and independently interpre…” · “Track record of owning features end-to-end —from initial technical design and implementation through rollout, monitoring, and sustained iteration.” · “You are a self-starter who has actively experimented with AI in work or personal projects and are excited to responsibly explore and define new AI tools and …”
Show it by: Takes a task from start to finish without being chased; Owns a mistake and what was changed after it.
Customer focus Core
In the posting: “We are evolving to act as a layer of product intelligence, making sense of customer data in order to personalize messages with the right item at the right ti…” · “Ensure data and service observability (metrics, logging, tracing, dashboards) to facilitate recommendations that are correct, explainable, fast, and highly a…” · “In addition to base salary, our total compensation package may include participation in the company’s annual cash bonus plan, variable compensation (OTE) for…”
Show it by: Puts the customer's need in their own words; Shows a result for the customer, not only for the company.
Data and analysis Core
In the posting: “We are evolving to act as a layer of product intelligence, making sense of customer data in order to personalize messages with the right item at the right ti…” · “Our systems span large-scale data pipelines and querying workflows, batch training and inference, and low-latency online retrieval and ranking systems.” · “Contribute to and maintain robust, large-scale data processing pipelines (e.g., using Apache Spark or similar frameworks) that transform raw events and catal…”
Show it by: Uses a number to describe the situation or the result; Explains what the data showed and what was done with it.
Attention to detail and quality Core
In the posting: “We are also building the experimentation, tracking, and measurement capabilities needed to evaluate recommendation quality and business impact over time.” · “Contribute to the architecture and evolution of backend services that power product recommendations across Klaviyo experiences (email, SMS, KAgent, onsite, e…” · “Contribute to and maintain robust, large-scale data processing pipelines (e.g., using Apache Spark or similar frameworks) that transform raw events and catal…”
Show it by: Gives an example of an error caught before it caused harm; Names the check or standard they used.
Stakeholder management Core
In the posting: “Collaborate closely with ML engineers and product stakeholders to productionize recommendation models —defining high-level interfaces, feature contracts, and…” · “Excellent technical collaborator and communicator: you can clearly articulate complex technical trade-offs to both technical peers and non-technical partners…”
Show it by: Shows how they aligned people with different goals; Explains how they handled a 'no' from someone senior.
Communication Supporting
In the posting: “Excellent technical collaborator and communicator: you can clearly articulate complex technical trade-offs to both technical peers and non-technical partners…”
Show it by: Adapts the message to the listener; Gives an example of explaining something clearly in writing or speech.
Leadership and mentoring Supporting
In the posting: “Share knowledge, mentor junior engineers, and define best practices on working with large-scale data frameworks, distributed systems, and integrating ML into…”
Show it by: Describes setting direction or a standard others followed; Names someone they coached and what changed for them.
Planning and organisation Supporting
In the posting: “In addition to base salary, our total compensation package may include participation in the company’s annual cash bonus plan, variable compensation (OTE) for…”
Show it by: Shows how priorities were set when everything was urgent; Mentions deadlines met and how.

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Practice questions

Tell me about yourself. Dedicated step-by-step practice.
If an interviewer asks something improper How to respond to questions about age, family, origin or health.
Built from this posting (8):
Walk me through a piece of work where you used your strongest technical skill. What exactly did you do? From this posting
Describe a time you worked with another team to deliver something. What was your part? From this posting
Tell me about a task you took from start to finish without anyone asking you to. From this posting
Describe a time you went out of your way to solve a customer's problem. From this posting
How have you used training in your work? Walk me through one real example. From this posting
Tell me about a problem you solved using python. From this posting
Tell me about a time you had to explain something complex to someone outside your field. From this posting
Tell me about a time you helped someone on your team get better at their work. From this posting
This posting reads as Software engineering: its questions come first.
Walk me through a feature you built from start to finish. What tools and languages did you use? Software engineering
Tell me about the hardest bug you have tracked down. How did you find the cause? Software engineering
Describe a technical design decision you made and the trade-off behind it. Software engineering
Tell me about a time you disagreed with a teammate in a code review. Software engineering
Production is down and you are the first engineer to notice. What do you do in the first 30 minutes? Software engineering
How do you decide what to test before you ship a change? Software engineering
Tell me about a time you helped a less experienced engineer get unstuck. Software engineering
Your estimate for a feature turns out to be badly wrong halfway through. What do you do? Software engineering
Tell me about a sale or agreement you are proud of.
Walk me through how you would check whether a report's numbers are right.
Describe a time you handled an upset customer or colleague.
Tell me about a time you had several deadlines at once. How did you decide what to do first?
Give an example of a mistake you caught before it caused a problem.
Tell me about a time you taught someone how to do something.
Describe a time you took the lead without being asked.
Tell me about a time you had to explain something complicated in simple terms.
Describe a process you improved.
Do you have any questions for us?
Why do you want to work here?
Why is there a gap in your employment? Difficult question
Why did you leave your last job? Difficult question
Why are you changing careers? Difficult question
What is your greatest weakness? Difficult question
What are your salary expectations? Difficult question
You seem overqualified for this role. Why do you want it? Difficult question
Improper-question practice: How old are you? / Do you have children? / Where are you from originally?
A customer is angry about a delay you did not cause. What do you do?
How do you keep track of many open customer requests?
Tell me about a time you could not give a customer what they wanted.
You find that the inventory count does not match the system. What do you do?
Tell me about a time a shipment or delivery went wrong.
How do you make sure safety rules are followed on a busy shift?
Two managers ask you to book the same room at the same time. What do you do?
How have you used spreadsheets in your work?
Tell me about a time you had to enter or check a lot of data accurately.
A prospect says your price is too high. How do you respond?
How do you keep your pipeline organised?
Tell me about a technical problem you solved on your own.
You find a bug right before a release. What do you do?