Remote Machine Learning Evaluation Engineer

OpenTrain AI, Inc.; United States; Remote; USD 138,000 - 207,000; Part time

Benefits offered by this job

Remote work

Flexible schedule

Portfolio building

Job summary

OpenTrain is seeking a Machine Learning Engineering Evaluator for a part-time, fully remote contractor role around 15 hours per week. You will develop and review ML models, pipelines, and evaluation systems using Python, PyTorch, and JAX, delivering rigorous technical assessments and documented trade-offs.

Flexible weekends may be available. The ideal candidate holds a Masters or PhD in a quantitative field, has practical ML experience, and can build reproducible workflows while explaining

Qualifications

• Advanced degree in a quantitative field; strong ML background.
• Proven ability to build reproducible ML workflows.
• Experience with multiple ML frameworks and inference tools.
• Ability to diagnose and explain performance trade-offs clearly.

Responsibilities

• Develop and validate ML models, training pipelines, and inference systems.
• Build reproducible workflows using Python and command-line tools.
• Analyze tensor operations, model architectures, and generation pipelines.
• Optimize latency, memory usage, and hardware utilization.
• Review AI-generated code and evaluate technical solutions.
• Design objective tests, benchmarks, and evaluation criteria.
• Document implementation choices, trade-offs, and failure modes.

Skills

Python proficiency

Debug ML systems

Explain trade-offs

Communication

Education

Master's or PhD in CS/ML

Tools

PyTorch

JAX

NumPy

Hugging Face

Job description

OpenTrain is seeking a Machine Learning Engineering Evaluator for a part-time, fully remote contractor role around 15 hours per week. You will develop and review ML models, pipelines, and evaluation systems using Python, PyTorch, and JAX, delivering rigorous technical assessments and documented trade-offs.

Flexible weekends may be available. The ideal candidate holds a Masters or PhD in a quantitative field, has practical ML experience, and can build reproducible workflows while explaining