Machine Learning Research & Engineering Specialist

About the role

Gramian Consultancy, a boutique consultancy in IT professional services and engineering talent solutions, is looking for an experienced Machine Learning Research & Engineering Specialist for an advanced AI training project. The project covers model development, training and inference systems, numerical computing and performance optimization. It is a coding-intensive, remote role open worldwide, requiring strong quantitative foundations and practical coding skills.

Key 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 workflows with Python, command-line tools and machine learning frameworks.
• Debug numerical, algorithmic and system-level failures and optimize training and inference for memory, efficiency, throughput and performance.
• Design and run tests to verify correctness and performance requirements.
• Review and validate ML engineering tasks, including existing code and proposed solutions.
• Use coding agents in the workflow to speed up implementation, debugging, testing and code review, while validating the results.
• Document technical approaches, decisions, test results and performance findings, and deliver reproducible task submissions on time.

Required profile

• Master's degree or PhD in a quantitative discipline such as computer science, machine learning, artificial intelligence, applied mathematics, statistics or engineering.
• Strong professional or research experience in machine learning and practical ML engineering.
• Availability of about 15 hours per week, starting the first task within 24-48 hours of onboarding.

Required skills

• Strong Python programming, including implementing, debugging, testing and optimizing non-trivial code.
• Hands-on experience with coding agents in a Python development workflow.
• Understanding of model development, training and inference workflows and evaluation methods.
• Ability to build reproducible implementations and diagnose numerical or computational issues.
• Strong written communication and independent work skills.