At Klaviyo, we love tackling tough engineering problems and look for employees who 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
The Data Science Platform team empowers our data scientists to build scalable, reliable, advanced, and iterative machine learning systems that automate and streamline Klaviyo's operations, make Klaviyo's product more intelligent, and transform Klaviyo's future. We support teams building systems like fraud detection, content understanding, forecasting, experiment optimization, and a host of other algorithms. Our efforts are focused on end-to-end use cases from rapid exploratory analytics to model deployment. We develop systems for experiment tracking, analysis environments, distributed training, and high-availability online model serving. Our stack includes tools like Kubernetes, Jupyter, MLFLow, Airflow, Ray and Spark and we assist in deploying models touching nearly every part of Klaviyo’s business and product.
As a Senior Machine Learning Engineer, 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.
As a senior team member you will help to level up our 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.
Please note that this role is based in Boston and requires a weekly hybrid, onsite component.
30 days
60 days
90 days
Up to 1 year
We encourage candidates to apply even if they do not meet all the qualifications listed below. ML Ops is a rapidly evolving space and we are all constantly learning!
*Please note: This role is also open to higher level candidates with an adjusted compensation range. Compensation is commensurate with experience. *
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