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

prolificacademicltd; USA; Remote; USD 66.000 - 110.000; Vollzeit

Benefits dieser Stelle

Fully remote

Flexible scheduling

Short sessions

Zusammenfassung

prolificAcademicLtd seeks seasoned machine learning engineers to audit AI-generated content, code, and reasoning in short remote sessions. You will apply deep technical judgment to evaluate explanations, training loops, and prompts within flexible project-based work.

Requirements include advanced degrees in CS/AI/related fields and production ML experience. Remote work is fully supported with sessions typically up to an hour, offering high-impact research contributions.

Qualifikationen

• BS, MS, or PhD in Computer Science, AI, Robotics, or related quantitative discipline with an ML focus
• Production-level experience building, deploying, or fine-tuning ML models
• Deep understanding of neural architectures (Transformers/CNNs/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 output
• Based in the Charlotte area

Aufgaben

• Evaluate the technical accuracy of AI 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 RLHF 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

Kenntnisse

ML engineering

Prompt engineering

RLHF

Model auditing

Attention to detail

Ausbildung

BS/MS/PhD in CS/AI/Robotics or related with ML focus

Tools

PyTorch

TensorFlow

Keras

Hugging Face Transformers

AWS SageMaker

Google Vertex AI

Weights & Biases

LangChain

Pinecone

Milvus

Weaviate

Jobbeschreibung

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 output
• Based 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