Staff Software Engineer - ML

Job Description Summary As a Staff Machine Learning Engineer, you will play a crucial role in bridging the gap between data science and production, ensuring the seamless integration and deployment of machine learning models into operational systems. You will be responsible for designing, implementing, and managing the infrastructure and workflows necessary to deploy, monitor, and maintain machine learning models at scale.

GE HealthCare is a leading global medical technology and digital solutions innovator. Our purpose is to create a world where healthcare has no limits. Unlock your ambition, turn ideas into world-changing realities, and join an organization where every voice makes a difference, and every difference builds a healthier world. Job Description

• Bachelors or Masters degree in Computer Science, Engineering, Mathematics, or related field.
• Strong programming skills in languages such as Python.
• Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Proficiency in cloud platforms such as AWS, Azure and related services (e.g., AWS SageMaker, Azure ML).
• Knowledge of containerization and orchestration technologies (e.g., Docker, Kubernetes).
• Familiarity with DevOps practices and tools (e.g., Git, Jenkins, Terraform).
• Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack).
• Familiarity with software engineering principles and best practices (e.g., version control, testing, debugging).
• Strong problem-solving skills and attention to detail.
• Excellent communication and collaboration skills.
• Ability to work effectively in a fast-paced and dynamic environment.

• Experience with big data technologies (e.g., Hadoop, Spark).
• Knowledge of microservices architecture and distributed systems.
• Certification in relevant technologies or methodologies (e.g., AWS Certified Machine Learning Specialty, Kubernetes Certified Administrator).
• Experience with data engineering and ETL processes.
• Understanding of machine learning concepts and algorithms.
• Understanding of Large Language Models (LLM) and Foundation Models (FM).
• Certification in machine learning or related fields.