Machine Learning Engineer

• Develop, implement, and validate machine learning models, training pipelines, inference systems, and supporting infrastructure.
• Implement or modify model components, data pipelines, evaluation systems, and numerical methods using Python.
• Build reproducible programmatic workflows using Python, command-line tools, and relevant machine learning frameworks.
• Debug numerical, algorithmic, and system-level failures to identify root causes and implement reliable solutions.
• Optimize model training and inference for memory usage, computational efficiency, throughput, and performance.
• Design and execute tests to verify implementation correctness and compliance with objective performance requirements.
• Review and validate challenging machine learning engineering tasks, including existing code and proposed solutions.
• Use coding agents as part of the development workflow to accelerate implementation, debugging, testing, and code review while validating generated results.
• Document technical approaches, implementation decisions, test results, and performance findings clearly.
• Deliver high-quality, reproducible task submissions within project deadlines.

Explore Machine Learning Research & Engineering Specialist at Gramian Consulting. Review requirements and application details.