Machine Learning Ops Engineer II

Key Skills

AWS Sagemaker, Data science techniques, Kubernetes-K8s, Machine learning techniques, MLflow, Python Programming, PyTorch, Ray.io, Scikit-learn, SPARK Programming

Job Description

About the role:

As a Machine Learning Engineer II, you will be a key contributor to the DS Platform team’s efforts to build and improve the tools, systems, and software services that Data Scientists depend on to create cutting edge models that power Klaviyo’s most advanced features.

You will be responsible for developing tools to train and develop models, serve models in production, and monitor models’ long term performance. You’ll work with a modern software stack built on Kubernetes, Sagemaker, MLFLow, Spark and Ray, helping to support models running on technologies such as PyTorch, ScikitLearn, Huggingface and more.

You will learn from senior team members and level up your software engineering, dev ops, and DS/ML skills in a collaborative hybrid environment surrounded by engineers and data scientists passionate about producing high quality and high value models and features.

Please note that this role is based in Boston and requires a weekly hybrid, onsite component.

How you'll have an impact:

30 days

• You spend the first two weeks in Klaviyo boot camp learning about the company and using the product.
• Your onboarding buddy and the team help you get contributing your first PR!
• You are participating in team meetings and processes and meeting key stakeholders in Data Science & Engineering teams.

60 days

• You own and ship your first feature contribution!
• You develop a career plan and personal goals with your manager.
• You have a firm understanding of at least one of the systems the team owns and are consistently contributing code.
• You are actively reviewing teammates’ code and helping them improve their engineering quality.
• You understand the team’s vision and roadmap.

90 days

• You own and ship multiple feature contributions, and you are shipping code independently.
• You are familiar with all of the team’s systems and have joined the team’s on-call rotation.
• You are actively reviewing teammates’ work and collaborating on multiple projects.
• You are contributing to roadmap planning, improving team processes and collaborating with Product and stakeholder teams on design.

Up to 1 year

• You successfully lead multiple projects that significantly improve the functionality and scalability of systems owned by Data Science Platform.
• You learn rapidly and help the team improve engineering quality, system stability and reliability.

What we're looking for:

We encourage candidates to apply even if they do not meet all the qualifications listed. ML Ops is a rapidly evolving space and we are all constantly learning!

• 1+ years industry experience as a software engineer
• Hands-on experience working with production environments
• Proficiency with Python or other programming languages
• Excitement to read the code, understand the product area and learn

Nice to have:

• Hands-on experience in shipping machine learning-based projects in production
• Experience with cloud infrastructure provisioning. We use AWS, Terraform and Kubernetes
• Experience collaborating with technical stakeholders, especially data scientists

The 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.

Base Pay Range For US Locations:

$123,200—$184,800 USD