Machine Learning Engineer optimizing multimodal embedding models for AI - driven video analysis at Twelve Labs. Collaborating with global teams to enhance production pipelines and model performance.
About the role
- Machine Learning Engineer at Grainger developing data pipelines and deploying machine learning models. Collaborating on CI/CD and real-time data integration for efficient processing solutions.
Responsibilities
- Design and implement distributed data pipelines using Apache Spark and Databricks
- Develop and deploy machine learning models for low-latency, real-time inference
- Construct and manage CI/CD pipelines for machine learning infrastructure
- Integrate and orchestrate asynchronous workflows and real-time data streams
Requirements
- Bachelor’s degree in Computer Science, Information Technology, Computer Engineering, Data Science, or related field plus 2 years of related experience
- Design and implement distributed data pipelines using Apache Spark and Databricks to ingest, transform, and store large volumes of structured and unstructured data across multi-stage environments
- Develop and deploy machine learning models for low-latency, real-time inference using Amazon SageMaker and containerized services, including the implementation of bring-your-own-container (BYOC) strategies and endpoint optimization
- Construct and manage CI/CD pipelines for machine learning infrastructure using tools such as ArgoCD, Helm, and GitHub Actions to automate model deployment, rollback, and lifecycle reproducibility across staging and production environments
- Integrate and orchestrate asynchronous workflows and real-time data streams using Apache Kafka, AWS Lambda, and Step Functions to enable feature computation, message-driven processing, and scalable ML inference pipelines
Benefits
- Up to 60% remote work allowed
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