Senior Machine Learning Engineer

September 20

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Everbridge

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Critical Communications • IT Alerting • Incident Management • Clinical Communications • Secure Messaging

1001 - 5000

Description

• Develop, deploy, and optimize NLP and other AI/ML models powering Everbridge’s risk intelligence automation tools and CEM analytics, planning, and forecasting products • Partner with a group of ML, software, and data engineers to define data models, acquire data sets, and build data pipelines necessary to power real-time data and analytics product capabilities • Collaborate with software and data engineers to design and implement scalable machine learning platforms and workflows for seamless deployment and monitoring. • Ensure models are robustly deployed using cloud-based platforms such as AWS SageMaker, and integrate with tools like Snowflake for data management and processing. • Mentor junior team members and establish machine learning engineering best practices, including a strong focus on MLOps principles (continuous integration, continuous delivery, monitoring).

Requirements

• Master's degree in a technical field (or equivalent work experience) • 5+ years of experience developing and deploying NLP models (e.g. BERT, BART, DistilBERT) in production • 5+ years of experience programming with Python in a production setting • 3+ years of hands-on experience with machine learning frameworks (e.g. PyTorch, TensorFlow) • Strong experience in deploying machine learning models using cloud platforms (e.g., AWS SageMaker, Azure ML) • Strong computer science, machine learning, and MLOps fundamentals • Experience with distributed data processing frameworks (e.g. Spark, Storm) and working with data platforms like Snowflake • Experience with Docker, Kubernetes, and Airflow for managing deployments and pipelines • Familiarity with GPU computing (i.e., CUDA and cuDNN)