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

prolificacademicltd; USA; Remote; USD 91.000 - 129.000; Teilzeit

Benefits dieser Stelle

Remote work

Flexible hours

Zusammenfassung

Prolific Academic Ltd. is seeking senior AI and Machine Learning Engineers to join an expert contributor network focused on training and evaluating next-generation large language models. Tasks vary from quick audits to sessions up to one hour, with fully remote, schedule-flexible work.

You'll review model architectures, audit code, and deliver high-quality feedback to power RLHF pipelines, helping improve alignment, safety, and helpfulness in production systems.

Qualifikationen

• Bachelor's/Master's/PhD in CS/AI/Robotics with ML focus.
• Production experience building, deploying, or fine-tuning ML models.

Aufgaben

• 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

Kenntnisse

Production experience ML models

Transformers, CNNs, RNNs and optim.

Prompt engineering, RLHF, RAG

Audit model logic and data quality

Attention to detail on AI outputs

Ausbildung

Bachelor's/Master's/PhD in CS/AI/Robotics with ML focus

Tools

PyTorch

TensorFlow/Keras

Python (NumPy, Pandas, scikit-learn)

Hugging Face Transformers

AWS SageMaker

Google Vertex AI

Weights & Biases

LangChain

Pinecone

Milvus

Weaviate

Jobbeschreibung

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