Machine Learning Research – Engineering Specialist

Gramian Consulting

. Develop, implement, and validate machine learning models, training pipelines, inference systems, and supporting infrastructure.

Posted 10/27/2026contractRemote • United States, Canada, Mexico, Costa Rica, Panama, GuatemalaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and validating machine learning models and workflows using Python, with a strong focus on optimization and systematic testing. Possesses advanced quantitative reasoning and problem-solving skills to address complex computational challenges.

Highest-signal resume keywords

Machine Learning Model DevelopmentPython ProgrammingMachine Learning FrameworksQuantitative ReasoningCoding Agents Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningModel TrainingInference SystemsData PipelinesNumerical MethodsDebuggingPerformance OptimizationSystem TestingReproducible WorkflowsAlgorithm Development

Soft Skills

Problem-SolvingWritten CommunicationIndependent WorkTask Management

Tools & Technologies

PythonCommand-Line ToolsMachine Learning Frameworks

Industry Keywords

Computer ScienceArtificial IntelligenceApplied MathematicsStatisticsEngineering

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.