AI Engineer/ML Engineer - Senior Developers - AI Training - Charlotte, US

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

Job summary

prolificacademicltd is seeking seasoned machine learning engineers to audit AI-generated content, code, and reasoning in short, remote sessions. The role involves evaluating explanations of architectures and training processes, and delivering high-impact feedback for RLHF pipelines.

Candidates should have production-level ML experience, a deep understanding of transformers/CNNs/RNNs, and practical exposure to prompt engineering and retrieval-augmented generation.

Qualifications

• Advanced degree with ML focus (BS/MS/PhD).
• Production-level experience with ML models: training, deployment or fine-tuning.
• Strong grasp of neural architectures (Transformers, CNNs, RNNs) and modern optimization methods.
• Familiarity with prompt engineering, RLHF or retrieval-augmented generation workflows.
• Meticulous attention to detail to catch hallucinations, bias, and logical flaws in AI output.

Responsibilities

• Evaluate accuracy of AI explanations on architectures, losses, and backpropagation.
• Audit ML codebases, training loops, and notebooks for correctness and performance.
• Provide high-quality human feedback for RLHF pipelines.
• Stress-test model reasoning on complex prompts and document failures.
• Compare model outputs using defined taxonomies and benchmarks.

Skills

ML model development

Prompt engineering

RLHF

Attention to detail

Audit ML outputs

Education

BS/MS/PhD in CS/AI/Robotics or related

Tools

PyTorch

TensorFlow

Keras

HuggingFace Transformers

AWS SageMaker

Vertex AI

Vector stores (Pinecone/Milvus/Weaviate)

Job description

Role overview

This paid, project-based opportunity invites seasoned machine learning engineers to contribute to the training and evaluation of advanced large language models. Participants apply deep technical judgment to audit AI-generated content, code, and reasoning in short, focused remote sessions. Work is intermittent and flexible, suiting practitioners who want to complement their primary role with high-impact research contributions.

Responsibilities

• Evaluate the technical accuracy of AI-generated explanations covering model architectures, loss functions, and backpropagation
• Audit ML codebases, training loops, and evaluation notebooks for correctness and performance
• Deliver high-quality human feedback that supports reinforcement learning from human feedback pipelines
• Stress-test model reasoning on complex chain-of-thought prompts and document where logic breaks down
• Compare outputs across models using defined taxonomies and performance benchmarks

Requirements

• BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative discipline with an ML focus
• Production-level experience building, deploying, or fine-tuning machine learning models
• Deep understanding of neural architectures including Transformers, CNNs, and RNNs, and modern optimization methods
• Practical exposure to prompt engineering, RLHF, or retrieval-augmented generation workflows
• Meticulous attention to detail for catching hallucinations, bias, and logical flaws in AI outputBased in the Charlotte area

Nice to have

• Expert-level command of PyTorch or TensorFlow and Keras
• Advanced Python skills across NumPy, Pandas, and Scikit-learn, with hands-on Hugging Face Transformers experience
• Familiarity with cloud ML tooling such as AWS SageMaker or Google Cloud Vertex AI, plus Weights & Biases or LangChain
• Working knowledge of vector stores such as Pinecone, Milvus, or Weaviate

Benefits and work setup

• Compensation typically up to $80 per hour for qualifying tasks
• Fully remote with flexible scheduling driven by project availability
• Sessions generally capped at one hour of uninterrupted focus, with many shorter in duration