About the role

As a Starburst Data Customer Support Engineer, you will be responsible for supporting our SEP (Starburst Enterprise Platform) and Galaxy customers that submit break/fix and other technical inquiries. You will work on a wide range of support matters including but not limited to configuration, security, integration and cluster issues to ensure our customers can maximize their experience with Starburst products. Additionally, this role serves as a liaison to ensuring that customers receive appropriate and timely service.

We’re looking for highly motivated individuals with backgrounds in deploying and debugging complex systems. Background in distributed systems, Hadoop, cloud technologies, security, DBMSs, and navigating a complex Java codebase are beneficial technical skills. Equally important are strong communication skills, and the ability to function in a fast-paced dynamic environment when needed. Trino encompasses a wide range of technologies, therefore a curiosity to learn and explore various approaches and solutions is a must, and most important is the desire and ability to learn and work cross-functionally in a highly customer-success-oriented company.

As a Customer Support Engineer at Starburst you will: 

• Respond to and resolve Galaxy & SEP customer inquiries via chat, email, or call session, within specified SLA windows
• Contribute to our customer-facing self-serve knowledge base
• Collaborate with our pre and post-sales teams to ensure cross-functional alignment and customer satisfaction
• Enthusiastically participate in ongoing personal learning including active participation in team training and development. 
• Escalate and manage escalated issues with Engineering to ensure good outcomes for our customers

Some of the things we look for: 

• Big Data (Hadoop, Data Lakes, Spark)
• Docker and Kubernetes
• Cloud technologies (AWS, Azure, GCP)
• Security
• Authentication (LDAP, OAuth2.0)
• Authorization technologies
• SSL/TLS
• Linux Skills
• DBMS Concepts/SQL Exposure 
• Languages: SQL, Java, Python, Bash

Where could this role be based?

United States - EST Locations

Ability to Travel: This role will require 25% in-person travel for purposes including but not limited to new hire onboarding, team and department offsites, customer engagements, and other company events. Actual travel expectations may vary by role and business needs.

At Klaviyo, we believe the future of software lies not in productivity tools for human users but in software that can run and optimize itself based on outcome or reward metrics. We’ve built the infrastructure and application that serve as the interface between businesses and consumers. We now have over 167,000 customers, billions of consumer profiles, and hundreds of billions of customer messages and follow-on conversion data. We have a big opportunity to build state-of-the-art AI and machine learning technologies at Klaviyo to power our products and develop AI agents that can automatically create and execute marketing or customer experiences, strategies, and campaigns for any business.

As a Sr. AI Engineer at Klaviyo, you’ll play a key role in designing and building scalable backend systems and user experiences that power our AI products and AI agent solutions. You’ll partner closely with product managers, machine learning engineers, and data scientists, to drive real-world impact at scale. This is a backend-heavy role with opportunities to influence architecture, async processing pipelines, distributed systems, and more.

How You Will Make a Difference

• Design and build backend systems that support scaling our AI solutions for 167K+ customers.

• Develop robust, reliable and scalable data collection and processing pipelines for machine learning models to train and consume.

• Develop robust, reliable and scalable services to serve AI models in production environments.

• Contribute to evolving our agentic architecture — making our AI agents more self-sufficient and performant.

• Contribute to a culture of ownership, experimentation, and customer-centric product thinking.

Who You Are

• 5–7 years of professional experience in software engineering, with a strong focus on backend systems and distributed architectures.

• Have hands-on experience building and deploying generative AI and agentic AI applications into production, with expertise in prompt engineering, few-shot learning, fine tuning and evaluation.

• Experienced backend engineer with a strong track record of building scalable, distributed systems, especially in the service of AI agent capabilities.

• Proficient in Python and modern backend frameworks (FastAPI, Django preferred).

• Experience creating human and automated evals to ensure high AI model quality.

• Proficient in big data tools such as Apache Spark and Hadoop.

• Deep experience with asynchronous processing and distributed task queues (Celery, Kafka, SQS, RabbitMQ, Redis).

• Strong understanding of database technologies and ORMs (SQLAlchemy, Alembic).

• Comfortable with cloud-native architectures (AWS) and container orchestration (Kubernetes); can manage infrastructure and CI/CD pipelines.

• Skilled at designing and building robust APIs.

• Able to operate with a high degree of autonomy, handle ambiguity, and thrive in a fast-moving, startup-like environment.

• Driven by curiosity. You're someone who learns and stays up to date with this rapidly evolving field.

• Comfortable collaborating directly with product managers and customers to shape solutions.

Nice to Have

• Trained ML models in the past and deployed them in production systems to generate impact to businesses.

• Experience in reinforcement learning.

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here

Lead AI Engineer

At Klaviyo, we believe the future of software lies not in productivity tools for human users but in systems that can learn, run, and optimize themselves based on measurable outcomes. We’ve built the infrastructure and applications that power the interface between businesses and consumers — supporting over 182,000 customers, billions of consumer profiles, and hundreds of billions of customer interactions.

Now, we’re building the next generation of AI and machine learning technologies to power these products — enabling AI agents that can automatically create, execute, and optimize marketing and customer experience strategies for any business.

As a Lead AI Engineer, you’ll take ownership of designing, building, and scaling the backend and machine learning systems that form the foundation of Klaviyo’s AI strategy. You’ll partner closely with product managers, data scientists, and engineers to deliver intelligent, outcome-driven systems that operate at massive scale — while mentoring others and setting technical direction for the team.

How You Will Make a Difference

• Lead the design and development of backend and ML systems that scale to serve 182K+ customers.
  
  
• Build robust, reliable, and efficient data processing and model-serving pipelines that power production ML models.
  
  
• Develop and optimize predictive systems (e.g., conversion likelihood, engagement ranking, personalization, anomaly detection).
  
  
• Architect systems and services that enable our AI agents to learn and improve autonomously from reward or outcome signals.
  
  
• Collaborate cross-functionally to translate business challenges into scalable, measurable ML solutions.
•  
• Collaborate with software engineers and product managers to turn predictive models into intuitive product features for our customers.
  
  
• Mentor engineers and help evolve Klaviyo’s ML engineering best practices, tooling, and architecture.
  
  
• Foster a culture of ownership, experimentation, and data-driven innovation across teams.
  
  

Who You Are

• 7–10 years of experience in machine learning engineering or applied software engineering, with a focus on classical ML and predictive modeling.
  
  
• Deep understanding of supervised learning methods such as random forests, XGBoost, logistic regression, and ensemble techniques.
  
  
• Proven track record of building and scaling production-grade ML systems end to end (data pipelines, training, deployment, and monitoring).
  
  
• Experienced backend engineer comfortable with distributed systems and high-scale architectures.
  
  
• Proficient in Python and modern frameworks (FastAPI, Django preferred).
  
  
• Skilled with big data and streaming technologies (Apache Spark, Kafka, Hadoop).
  
  
• Hands-on with asynchronous processing and distributed task queues (Celery, SQS, RabbitMQ, Redis).
  
  
• Strong understanding of databases and ORMs (SQLAlchemy, Alembic) and cloud-native architectures (AWS, Kubernetes, CI/CD).
  
  
• Excellent communicator who can set technical direction, influence product strategy, and mentor junior engineers.
  
  
• Curious, pragmatic, and passionate about building AI systems that drive measurable business impact.
  
  

Nice to Have

• Experience training and deploying ML models in production environments that generated measurable impact.
  
  
• Familiarity with reinforcement learning or agentic AI systems that adapt based on outcome or reward signals.
  
  

#indeedRD

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here

Job Title: Sr. Lead AI Engineer
Location: Boston, MA

This role is based in our Boston, MA hub (Hybrid-3x week). 

About the Role

At Klaviyo, we believe the future of software isn’t just better productivity tools for humans—it’s systems that can run, optimize, and adapt themselves based on outcome and reward signals. We’ve already built the infrastructure and applications that sit between businesses and consumers—serving 183,000+ customers, billions of consumer profiles, and hundreds of billions of messages and conversion events. Now we’re investing heavily in Marketing AI and Service AI: the shared services and agentic capabilities that power AI‑native experiences across Klaviyo.

As a Sr. Lead AI Engineer in the AI & Analytics organization, you’ll be the technical leader for a set of high‑impact AI services and agentic systems. You’ll own architecture and execution for complex, distributed backend systems; guide other engineers through design and implementation; and partner closely with product, machine learning, and data science to turn AI ideas into reliable, scalable production capabilities. This is a hands-on tech lead role, not a people‑manager role.

How You’ll Make a Difference

• Define and improve the engineering architecture for Service and Marketing AI capabilities. Lead the design of scalable, low‑latency backend systems and APIs that power our AI products and agents for 183K+ customers, handling billions of events and interactions.
• Build and harden AI serving systems. Lead the development of services that host and orchestrate AI models (LLMs, tools, evaluators, retrieval systems), with clear contracts, SLOs, and observability so other teams can depend on them.
• Evolve our agentic architecture. Drive the technical roadmap for Service and Marketing AI agents—improving tool use, autonomy, and reliability so agents can safely take actions on behalf of customers and internal users with minimal human intervention.
• Set engineering standards for AI services. Establish best practices for evaluation, safety/guardrails, prompt and model versioning, offline and online tests, and incident response for AI‑backed systems; hold the bar for design reviews and code quality.
• Lead cross‑team technical work. Act as the primary technical interface between Service / Marketing AI and other product/platform teams—clarifying ownership boundaries, aligning on interfaces, and unblocking dependencies across hubs and time zones.
• Mentor and uplevel engineers. Coach senior and mid‑level engineers through design, implementation, and operational best practices, helping them grow into stronger system owners and future tech leads.
• Help build the Palo Alto hub. Shape local engineering rituals, help with hiring and onboarding, and create healthy patterns for cross‑hub collaboration so work and career opportunities are not constrained by geography.
• Measure what matters. Define and track key metrics for your systems—availability, latency, cost‑to‑serve, agent success rates, eval scores, and customer adoption—and use those insights to drive roadmap and technical decisions.
• Transform engineering and product development workflows by putting AI at the center, building & utilizing agentic coding tools and AI-first working.

Who You Are

• Seasoned backend engineer & tech lead. 8+ years of professional software engineering experience with a strong focus on backend and distributed systems; you’ve led complex projects or areas end‑to‑end and acted as the go‑to technical owner for key services.
• Hands‑on with generative & agentic AI in production. You’ve built and shipped generative or agentic AI applications (e.g., LLM‑backed flows, tool‑using agents, retrieval‑augmented systems) and are comfortable with prompt design, few‑shot approaches, fine‑tuning, and evaluation.
• Deep experience with large‑scale distributed systems. You’ve architected and operated reliable services, async processing pipelines, and distributed task queues (e.g., Celery, Kafka, SQS, RabbitMQ, Redis) supporting high‑throughput workloads.
• Strong Python and data tooling fluency. Proficient in Python and modern backend frameworks (FastAPI, Django or similar), with experience using big data tools such as Spark/Hadoop and ORMs like SQLAlchemy/Alembic.
• Production‑grade cloud experience. Comfortable with AWS and Kubernetes, CI/CD pipelines, observability, and operational best practices; you treat infrastructure choices as part of the system design, not an afterthought.
• Evaluation and quality‑obsessed. You’ve designed human and automated evals for AI systems, know how to instrument for quality, and understand how to balance latency, cost, and response quality in real‑world usage.
• Technical leader, not just individual contributor. You influence architecture beyond your own code, facilitate design reviews, rally cross‑functional partners, and help teams converge on pragmatic decisions in ambiguous spaces.
• Collaborative and customer‑first. You’re comfortable collaborating directly with PMs, ML engineers, and customers; you can translate between customer needs, product goals, and technical constraints, and you care deeply about the customer experience.
• You’ve already been effectively practicing agentic coding in your daily work. You’re hungry to responsibly explore new AI tools and workflows, finding ways to make your work smarter and more efficient.
• Bonus: You use AI tools to accelerate exploration, shorten iteration cycles, and bring sharper ideas to the table—for yourself and your teammates.

Nice to Haves

• Prior experience training and deploying ML models (including RL or RLHF) into production systems that drove measurable business impact.
• Experience building shared AI/ML platforms or services used by multiple product teams.
• Background in marketing tech, ecommerce, or other domains where customer data, personalization, and experimentation are central.

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here