Machine Learning Engineer

• Bridge cutting-edge machine learning research and robust production deployment
• Develop production deployment systems for classical and machine learning algorithms
• Build robust, scalable inference pipelines
• Develop primarily in Python and infrastructure tools such as Kubernetes and Docker
• Maintain existing deployment systems and develop new production capabilities
• Learn and implement new deployment technologies and best practices
• Stay current with MLOps and inference optimization techniques
• Address emerging production challenges
• Stay informed about emerging computer vision and NLP solutions

Machine Learning Software Engineer

• Develop production deployment systems for classical and machine learning algorithms from research.
• Build robust, scalable inference pipelines.
• Develop primarily in Python and infrastructure tools such as Kubernetes and Docker.
• Maintain existing deployment systems and develop new production capabilities.
• Learn and implement new deployment technologies and best practices for emerging production challenges.
• Stay current with the latest MLOps and inference optimization techniques.
• Bridge cutting-edge machine learning research and robust production deployment.
• Develop production solutions that serve algorithms at scale.