Lead ML Ops Engineer

Key Skills

AWS, Deep Learning, GPUs, HuggingFace, Jenkins, Kubernetes-K8s, Leadership, Machine learning techniques, Mentoring, MLflow, MLOps tools, Python Programming, Ray.io, Terraform

Job Description

 About the team:

Klaviyo operates a real-time data analytics platform coded primarily in Python that is built for massive scale and hosted on Amazon Web Services (AWS). Engineers come to Klaviyo with experience in a variety of languages and from a number of disciplines.

At Klaviyo, we love tackling tough engineering problems and look for employees who specialize in certain areas but are passionate about building, owning & scaling features end to end from scratch and breaking through any obstacle or technical challenge in their way. We push each other to move out of our comfort zone, learn new technologies, and work hard to ensure each day is better than the last. Learn more about our engineering culture at https://klaviyo.tech

About the role:

This Lead MLE is responsible for technical leadership on the team of Machine Learning Engineers who build and maintain the services that enable and accelerate Data Science and Machine Learning at Klaviyo, including tooling for training, testing, serving, and monitoring models. The candidate should have at least 5 years of technical expertise in MLOps or adjacent areas. Please note that this role is based in Boston and requires a hybrid, onsite component.

How you'll have an impact:

30 days

You will have set up your local environment and contributed your first PR. You will be participating in team meetings and processes, and have met several members of the wider Data Science team.

60 days

You will have successfully contributed multiple PRs to projects in flight. You have met with stakeholders such as Data Scientists and Data Infrastructure engineers in order to gather the necessary context for future projects and understand requirements or constraints on system design imposed by those dependencies. You will have a firm understanding of at least one of the systems the team owns, and will be actively and consistently contributing code on a regular cadence. You will be actively reviewing teammates’ code and helping them improve their engineering quality.

90 days

You will have developed at least two majors feature. You will be familiar with all of the team’s systems and have joined the team’s on-call rotation. You will be a leading voice on team discussions. You will have started to formulate a technical vision for priorities for the upcoming quarter of work. You understand what areas have gaps that need to be filled and where there are opportunities to improve functionality, scalability, reliability, etc.

Up to 1 year

You will have successfully led the team to deliver multiple high impact projects that significantly improve the functionality and scalability of systems owned by DS Platform. You work with the team and EM to develop and maintain short, medium, and long-term technical roadmaps. The team’s engineering quality, and system stability and reliability have noticeably improved because of your guidance and technical leadership.

What we're looking for:

• Minimum 5 years experience in ML Ops
• Recent experience in MLOps that demonstrates technical leadership
• Experience putting machine learning models with a variety of applications and usage patterns into production
• Proficient with Python, AWS, Terraform, Kubernetes
• Excited to read the code, understand the product area, and give applicable technical mentorship.

Nice to have:

• Experience with Jenkins, Ray, Spark, Kubeflow, MLFlow, Huggingface, GPU, Deep Learning

The starting pay range for this role is listed below. Sales roles are also eligible for variable compensation and hourly non-exempt roles are eligible for overtime in accordance with applicable law. This role is eligible for benefits, including: medical, dental and vision coverage, health savings accounts, flexible spending accounts, 401(k), flexible paid time off and company-paid holidays and a culture of learning that includes a learning allowance and access to a professional coaching service for all employees.

Starting Pay Range For All US Locations:

$192,000—$288,000 USD