About Gramian
Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs.
About the Role
We are seeking a highly skilled Machine Learning Research & Engineering Specialist to contribute to an advanced AI training project focused on model development, training and inference systems, numerical computing, and performance optimization. This is a coding-intensive role requiring hands-on experience with Python, machine learning engineering, and coding agents integrated into development workflows. You will build, modify, debug, and validate machine learning implementations, create reproducible training and inference pipelines, and optimize systems for correctness, memory usage, and throughput. The ideal candidate combines strong quantitative foundations with practical coding skills and experience solving complex machine learning engineering problems.
SENIORITY: Experienced / Research-Level Engineer
EDUCATION: Master's degree or PhD in a relevant quantitative discipline
LANGUAGE: Strong written and verbal English
LOCATION: Remote — Worldwide
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 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.
Requirements
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.
Availability to contribute approximately 15 hours per week and begin the first task within 24–48 hours after onboarding.
Develop and optimize machine learning models, training and inference systems, and reproducible Python workflows, while debugging numerical issues and validating correctness, performance, and reliability.
Develop and validate machine learning models, training and inference pipelines, and numerical systems, while debugging implementations and optimizing correctness, memory use, throughput, and performance.
Develop and validate machine learning models, training and inference pipelines, and numerical systems; debug complex failures and optimize implementations for correctness, memory use, throughput, and performance.
Own the software and infrastructure that take machine learning models from experiments to large-scale training and production inference, enabling research scientists to build, evaluate, and serve models reliably.
Develop and operate production email-security models and Go-based filtering systems, improving spam and phishing detection while balancing catch rates against false positives.
