Smarsh is hiring a
Machine Learning Framework Engineer

Summary

Join Smarsh as an ML Infrastructure Engineer in their Applied Machine Learning team. Contribute to the development and maintenance of tools and infrastructure that empower Data Scientists and Research Engineers. Requires a degree in Computer Science, Engineering, Data Science or related field, extensive experience in building and managing machine learning infrastructure and tooling, proficiency in AWS, software engineering, machine learning frameworks, data management and pipeline orchestration tools, and excellent communication skills.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science or a related field
• Extensive experience in building and managing machine learning infrastructure and tooling
• Deep knowledge of MLOps best practices, including model deployment, monitoring, and scaling
• Strong proficiency in AWS, with a proven track record of managing and optimizing cloud-based machine learning environments
• Expertise in software engineering, including Python and other programming languages commonly used in machine learning
• Excellent understanding of machine learning frameworks such as PyTorch, and Scikit-Learn, CUDA, Triton and TensorFlow
• Experience with data management and pipeline orchestration tools (e.g., Airflow, Kubeflow)
• Strong problem-solving skills and the ability to work in a fast-paced, collaborative environment
• Exceptional communication skills with a demonstrated ability to work effectively in a team-oriented environment

Responsibilities

• Contribute to and oversee internal machine learning libraries to ensure scalability and efficiency across the team
• Collaborate with Data Scientists and Research Engineers to evaluate, select, and integrate machine learning tools and frameworks
• Manage and optimize AWS infrastructure to support scalable, high-performance machine learning applications
• Enable highly parallelized experiments to scale efficiently across CPU and GPU resources
• Design and build end-to-end pipelines for model training, evaluation, hyperparameter optimization, bias detection, and report generation
• Maintain dataset management tools to power our data strategy
• Incorporate and manage experiment tracking systems to support research and development
• Ensure model building processes are enterprise-grade and repeatable
• Work closely with production engineering teams on end-to-end MLOps and to establish effective contracts and connection points
• Integrate and coordinate various components to build a cohesive, efficient machine learning infrastructure

Benefits

• Healthcare insurance
• Personal time off
• 401K Match
• Sabbatical
• Recognition