Remote Senior Machine Learning Infrastructure Engineer

Description:

• Design and architect scalable and reliable infrastructure solutions to support machine learning workflows, including data ingestion, model training, evaluation, and deployment.
• Develop and maintain data pipelines to ingest, preprocess, and transform data for training machine learning models, ensuring data quality, integrity, and scalability.
• Build and optimize infrastructure for training machine learning models at scale, leveraging distributed computing frameworks and accelerators for performance and efficiency.
• Design and implement systems for deploying and managing machine learning models in production environments, ensuring reliability, scalability, and real-time inference capabilities.
• Implement monitoring and logging solutions to track the performance and health of machine learning infrastructure and models, proactively identifying and resolving issues.
• Develop automation and orchestration tools to streamline machine learning workflows, reducing manual intervention and improving operational efficiency.
• Implement security controls and ensure compliance with data privacy regulations in machine learning infrastructure and workflows, protecting sensitive data and ensuring regulatory compliance.
• Document infrastructure designs, processes, and best practices, providing clear and comprehensive documentation to facilitate understanding and collaboration among team members.
• Collaborate with data scientists, machine learning engineers, and software developers to understand requirements and deliver infrastructure solutions that meet business needs.
• Mentor junior engineers, sharing expertise and best practices in machine learning infrastructure engineering, and facilitate knowledge sharing sessions within the team.

Requirements:

• Bachelor's degree or higher in Computer Science, Engineering, Mathematics, or related field.
• 5+ years of experience in infrastructure engineering, with a focus on machine learning infrastructure.
• Proficiency in cloud platforms such as AWS, Azure, or Google Cloud Platform, and services like AWS SageMaker, Azure Machine Learning, or Google AI Platform.
• Strong programming skills in languages such as Python, Java, or Scala, with experience in distributed computing frameworks like Apache Spark or TensorFlow.
• Experience with containerization technologies such as Docker and container orchestration platforms such as Kubernetes.
• Strong understanding of machine learning concepts and techniques, with experience deploying and managing machine learning models in production environments.
• Strong problem-solving skills and analytical thinking, with the ability to design and troubleshoot complex infrastructure issues.
• Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams and communicate technical concepts to non-technical stakeholders.

Benefits:

• Competitive salary: The industry standard salary for Senior Machine Learning Infrastructure Engineers typically ranges from $170,000 to $230,000 per year, depending on experience and qualifications.
• Comprehensive health, dental, and vision insurance plans.
• Flexible work hours and remote work options.
• Generous vacation and paid time off.
• Professional development opportunities, including access to training programs, conferences, and workshops.
• State-of-the-art technology environment with access to cutting-edge tools and resources.
• Vibrant and inclusive company culture with opportunities for growth and advancement.
• Exciting projects with real-world impact at the forefront of AI-driven innovation.