Note: The job is a remote job and is open to candidates in USA. Gramian Consultancy is seeking a Machine Learning Research & Engineering Specialist for an advanced AI training project. The role develops and validates machine learning models, training pipelines, and inference systems, while debugging implementations and optimizing performance, memory usage, and throughput.
Responsibilities
- 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
Skills
- Master's degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or a closely related quantitative discipline
- Strong professional or research experience in machine learning and practical ML engineering
- Strong Python programming skills, including the ability to implement, debug, test, and optimize non-trivial code
- Hands-on experience using coding agents within a Python development workflow
- Understanding of machine learning model development, training and inference workflows, and evaluation methods
- Ability to build reproducible implementations and verify correctness through systematic testing
- Strong quantitative reasoning and problem-solving skills, particularly when diagnosing numerical or computational issues
- Ability to work independently, manage technical tasks, and meet defined quality and submission requirements
- Strong written communication skills and the ability to explain technical findings
Benefits
- Remote — Worldwide
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