Senior AI Engineer - Remote Research & RLHF Evaluation

prolificacademicltd; United States; Remote; USD 66,000 - 110,000; Full time

Benefits offered by this job

Remote work

Flexible hours

Short onboarding

Job summary

prolificacademicltd is recruiting senior AI and ML Engineers to support training and evaluation of advanced large language models. You will work remotely on short, task-based studies with flexible hours, earning per task compensation up to $80/hour.

Responsibilities include auditing ML code and notebooks, validating model explanations, and providing high-quality feedback to RLHF pipelines to improve model alignment.

Qualifications

• PhD/MS/BS in CS/AI/related quantitative field with ML focus.
• Production-level experience building, deploying, or fine-tuning ML models.
• Strong command of Transformer, CNNs, RNNs architectures and their optimization.
• Hands-on prompt engineering, RLHF, or retrieval-augmented generation workflows.

Responsibilities

• Evaluate AI-generated explanations of model architectures, loss functions, and backpropagation for technical correctness.
• Audit ML code and notebooks, including training loops, preprocessing scripts, and evaluation harnesses, for quality and efficiency.
• Contribute high-quality human feedback that informs RLHF pipelines and strengthens model alignment.
• Diagnose how a model handles chain-of-thought prompts and flag where its reasoning fails.
• Run structured benchmarks across model outputs based on technical taxonomies and metrics.

Skills

ML research

Prompt engineering

RLHF

Model evaluation

Attention to detail

Education

BS/MS/PhD in CS/AI/related quantitative field

Tools

PyTorch

TensorFlow/Keras

Hugging Face Transformers

NumPy

Pandas

Scikit-learn

Job description

prolificacademicltd is recruiting senior AI and ML Engineers to support training and evaluation of advanced large language models. You will work remotely on short, task-based studies with flexible hours, earning per task compensation up to $80/hour.

Responsibilities include auditing ML code and notebooks, validating model explanations, and providing high-quality feedback to RLHF pipelines to improve model alignment.