Machine Learning Research & Engineering Specialist
New
G
Gramian ConsultancyMachine learning
Workable locations: India. Australia. Japan. Singapore. Philippines. New ZealandContractMiddle
Salary not disclosed
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Job Details
- Languages
- Strong written and verbal English
- Required Skills
- PythonMachine Learning
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 implementing, debugging, testing, and optimizing 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 ability to explain technical findings.
- Availability to contribute approximately 15 hours per week and begin the first task within 24–48 hours after onboarding.
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, 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 machine learning engineering tasks, existing code, and proposed solutions.
- Use coding agents in 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.
- Deliver high-quality, reproducible task submissions within project deadlines.
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