Responsibilities

• Design and develop machine learning infrastructure.
• Own and enhance core components of the ML infrastructure, including systems for offline and online model training, model pipeline health monitoring, model serving, feature authoring, and feature serving.
• Proactively address ML infrastructure issues that may impact production.
• Collaborate with ML engineers to build robust model pipelines utilizing the ML infrastructure.

Requirements

• Bachelor’s degree in Computer Science, Engineering, or a related field with 2+ years of relevant work experience; or a Master’s/PhD in a related discipline.
• Familiarity with software development processes, including version control, bug tracking, and design documentation.
• Proficient in Python, with a strong understanding of object-oriented programming and microservices architecture.
• Basic knowledge of Applied Machine Learning and experience with major Deep Learning frameworks, such as PyTorch and TensorFlow.

Preferred qualifications

• Proven track record in building large-scale distributed backend systems.
• Familiarity with cloud services such as AWS, GCP, Azure, and others.
• Contributions to open-source machine learning or infrastructure tools.
• A systematic, data-driven problem-solving approach combined with strong communication skills.

Benefits