AI training jobs for machine learning engineers

Evaluate AI-generated clinical content on heart failure diagnosis and management, and provide evidence-based feedback to improve AI medical recommendations.

Evaluate AI-generated interventional cardiology information and review scenarios involving coronary angiography, angioplasty, and stent placement.

Design model training and data pipeline tasks with runnable reference solutions and develop rubrics for reproducibility.

Review documents and judge whether their content and structure reflect material encountered in day-to-day data science practice.

Develop and validate machine-learning models, training pipelines, and inference systems, and build reproducible workflows using Python.

Develop and validate machine-learning models and training pipelines, and build reproducible Python workflows for AI training projects.

Develop and validate machine-learning models and training pipelines, and build reproducible Python workflows for AI training projects.

Evaluate and refine AI-generated technical design documents and data pipeline and ETL documentation.

Create self-contained problems involving biomedical signals and time-series data, and develop reference solutions using Python or MATLAB.

Build reproducible environments and reference solutions, then develop tests that distinguish correct solutions from flawed approaches for data science tasks.

Develop realistic multi-step finance prompts, create rubrics, and evaluate AI agent responses with structured feedback.

Write modeling problems with reference answers and grade model-written training and evaluation code, checking for eval set leaks.

Evaluate and refine AI-generated technical design documents and ETL documentation to help train a language model.

Create, evaluate, and refine AI-generated technical design documents and data pipeline and ETL documentation.

Review data science documents and judge whether their content and structure reflect material encountered in day-to-day data science practice.

Review and assess data science documents, judging whether their content and structure reflect material encountered in day-to-day practice.

Review and assess data science documents, judging whether their content and structure reflect material encountered in day-to-day data science practice.

Design grading criteria for data science deliverables and evaluate AI-generated or human-created work against established criteria.

Review AI-generated Python code and machine learning workflows against project guidelines, and provide written feedback to trainers on quality issues.

Review AI-generated data science content and trainer work against project guidelines, and provide precise written feedback on quality issues.

Review AI-generated analytical reasoning, code, and model outputs for errors, and write step-by-step reference solutions for complex data problems.

Train and evaluate AI models in Machine Learning Engineering by creating tasks and deliverables from real-world scenarios.