AI Engineer/ML Engineer - Senior Developers - AI Training - El Paso, US

prolificacademicltd; United States; Remote; USD 69,000 - 110,000; Full time

Benefits offered by this job

Hourly pay up to $80

Fully remote

Flexible hours

Short skills onboarding

Job summary

Prolific Academic Ltd is seeking senior AI/ML engineers to join a fully remote expert contributor network. You’ll tackle paid tasks ranging from quick audits to hour-long sessions, contributing to training and evaluation of next‑gen language models.

Responsibilities include auditing training loops and notebooks, delivering high-quality feedback for RLHF, and assessing model reasoning. Flexible hours suited for experienced researchers with production ML experience.

Qualifications

• Degree in CS/AI/Robotics or related quantitative field with ML focus
• Production ML experience with deployment or fine-tuning
• Knowledge of transformer architectures and optimization techniques
• Hands-on experience with prompt engineering, RLHF, or RAG workflows
• Ability to audit model logic and detect data contamination
• Attention to detail for hallucinations and biases in AI content

Responsibilities

• Review explanations of model architectures, loss functions, and backpropagation for accuracy
• Audit ML code and notebooks for correctness and efficiency
• Provide high-quality human feedback to RLHF pipelines to improve alignment
• Assess chain-of-thought reasoning and identify logical breakdowns
• Run benchmarks across model outputs using defined metrics

Skills

Prompt engineering

RLHF

RAG workflows

Model evaluation

Education

Bachelor's / Master's / PhD in CS/AI/Robotics

Tools

PyTorch

TensorFlow/Keras

Hugging Face Transformers

AWS SageMaker / Vertex AI

LangChain / VectorDBs (Pinecone, Milvus, Weaviate)

Job description

Role overview

This opportunity invites senior AI and Machine Learning Engineers into an expert contributor network focused on training and evaluating next-generation large language models. Participants take on paid technical assignments that draw on production ML experience, with task lengths ranging from short audits to sessions lasting up to one hour. The work is fully remote and schedule-flexible, allowing experienced engineers to contribute alongside other commitments.

Responsibilities

• Review AI-generated explanations of model architectures, loss functions, and backpropagation for technical accuracy
• Audit ML code and notebooks, including training loops, preprocessing scripts, and evaluation routines, for correctness and efficiency
• Deliver high-quality human feedback that powers RLHF pipelines and improves model alignment, safety, and helpfulness
• Critically assess chain-of-thought reasoning and identify exactly where model logic breaks down
• Run comparative benchmarks across model outputs using defined technical taxonomies and performance metrics

Requirements

• Bachelor's, Master's, or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative field with a machine learning focus
• Production experience building, deploying, or fine-tuning machine learning models
• Professional-level understanding of neural network architectures such as Transformers, CNNs, and RNNs, along with modern optimization techniques
• Hands‑on experience with prompt engineering, RLHF, or retrieval‑augmented generation workflows
• Ability to audit complex model logic, identify training data contamination, and assess mathematical proofs behind ML algorithms
• Sharp attention to detail for catching hallucinations, biased outputs, and logical failures in AI‑generated technical content

Nice to have

• Expert proficiency with PyTorch or TensorFlow/Keras
• Advanced Python skills, including NumPy, Pandas, and Scikit‑learn, plus experience with Hugging Face Transformers
• Familiarity with AWS SageMaker, Google Cloud Vertex AI, Weights & Biases, or LangChain
• Experience with vector databases such as Pinecone, Milvus, or Weaviate for RAG evaluation

Benefits and work setup

• Hourly compensation reaching up to $80 per hour depending on the researcher and task
• Fully remote with flexible hours and the ability to work from home
• Onboarding begins after a short 10-15 minute skills assessment; participants choose tasks that fit their availability