AI training jobs for machine learning engineers

Review and rank technical slide decks from your field to determine what makes an excellent technical presentation.

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.

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

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

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

Evaluate AI model outputs on statistics and machine learning problems and create expert-level prompts and reference solutions.

Evaluate AI model outputs on data science and statistics problems and create expert-level prompts, datasets, and reference solutions.

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.

Author and solve data analysis problems for AI evaluations, covering experiments, metrics, forecasting, and predictive models.

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

Author and solve analysis problems drawn from research on experiment design, ablations, evaluation and model behavior.

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.

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

Design a demanding AI prompt from a real finance workflow, test it against ChatGPT, and build a grading rubric.

Construct structured environments for reinforcement learning platforms and evaluate agent interactions within scenario generation and world-building studies.

Design and refine machine learning models in Python, then tune hyperparameters and benchmark results.

Create a demanding prompt based on your real-world workflows, test it against ChatGPT, and build a grading rubric.

Build realistic scenarios and workflows for reinforcement learning environments and review tasks created by other participants.

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

Design evaluation scenarios, write reference solutions, and grade model outputs on LLM inference and GPU performance problems.

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.

Complete model development, pipeline construction, and research tasks on genomic, transcriptomic, or multi-omics datasets.

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