Machine Learning Operations Engineer

CarGurus•Boston, MA

2h•$133,000 - $167,000•Hybrid

About The Position

Support data scientists to deploy machine learning (ML) models to production and to build and maintain the APIs and data pipelines that integrate predictive intelligence into CarGurus’ products. Duties include: enhance and maintain CarGurus’ cloud-hosted ML platform; support systems recommendations including search ranking, computer vision, and instant market value; develop, implement, and integrate production-quality training jobs and inference APIs for Python ML models, to build and deploy robust and scalable data; implement enhancements to ML platform, leveraging technologies such as AWS SageMaker, GitHub Actions, and Docker; support design conversations and collaborate with data scientists and engineering partners, to design scalable and robust systems; develop in-house tools and libraries to standardize and accelerate the ML development process; and maintain aspects of data science team engineering infrastructure. Multiple positions.

Requirements

Master’s degree (or foreign equivalent) in Computer Science, Data Science Computer Engineering, Electrical Engineering, or a related field
Two (2) years of experience in the job offered or related occupation
Two (2) years of experience with writing and debugging Python code
Two (2) years of experience with software engineering tools and standard methodologies, including Unit testing, object-oriented design, and containerization
Two (2) years of experience with Machine learning (ML) lifecycle, including model training, evaluation, deployment, and monitoring
Two (2) years of experience with Python ML ecosystem, such as scikit-learn, XGBoost, PyTorch, numpy, or pandas
Two (2) years of experience deploying, monitoring, and troubleshooting ML models in public cloud platforms, such as AWS
Two (2) years of experience with SQL and cloud data warehouses, such as Snowflake

Responsibilities

Enhance and maintain CarGurus’ cloud-hosted ML platform
Support systems recommendations including search ranking, computer vision, and instant market value
Develop, implement, and integrate production-quality training jobs and inference APIs for Python ML models
Build and deploy robust and scalable data
Implement enhancements to ML platform, leveraging technologies such as AWS SageMaker, GitHub Actions, and Docker
Support design conversations and collaborate with data scientists and engineering partners, to design scalable and robust systems
Develop in-house tools and libraries to standardize and accelerate the ML development process
Maintain aspects of data science team engineering infrastructure