AI Engineer/ML Engineer - Senior Developers - AI Training - Ireland

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

Job summary

Prolific Academic Ltd is seeking senior AI and Machine Learning Engineers to train, critique, and benchmark large language models on a paid, task-based basis. Work is fully remote with flexible scheduling, letting you choose tasks that fit within roughly an hour of focused effort.

You will audit architectures, code, and RLHF signals, compare outputs against structured taxonomies, and provide precise feedback to improve safety and usefulness.

Qualifications

• BS, MS, or PhD in Computer Science, AI, Robotics or related quantitative field with ML concentration.
• Hands-on production experience building, deploying, or fine-tuning ML models.
• Knowledge of neural network architectures such as Transformers, CNNs, RNNs, and modern optimization techniques.
• Practical experience with prompt engineering, RLHF, or retrieval-augmented generation workflows.
• Ability to audit intricate model logic, detect data contamination, and reason about ML proofs.

Responsibilities

• Scrutinize AI-generated descriptions of model architectures, loss functions, and backpropagation for technical accuracy.
• Audit ML code and notebooks for efficiency and correctness.
• Supply human feedback signals that drive RLHF alignment and model safety.
• Analyze how models navigate chain-of-thought prompts and identify failure points.
• Benchmark model outputs against structured technical taxonomies and quantitative criteria.

Skills

Transformers

RNNs

CNNs

prompt engineering

RLHF

Model auditing

Education

BS/MS/PhD in CS/AI/Robotics with ML concentration

Tools

PyTorch

TensorFlow/Keras

AWS SageMaker

Google Vertex AI

Weights & Biases

LangChain

Pinecone

Milvus

Weaviate

Job description

Role overview

A specialist expert network is looking to onboard senior AI and Machine Learning Engineers who can train, critique, and benchmark large language models on a paid, task-based basis. Participants contribute their production ML knowledge to structured research studies, with assignments designed to fit within roughly an hour of focused effort. The role is fully remote, with flexible scheduling that lets engineers pick up work when it suits them.

Responsibilities

• Scrutinize AI-generated descriptions of model architectures, loss functions, and backpropagation for technical accuracy
• Audit ML code and notebooks, spanning training loops, preprocessing scripts, and model evaluations, for efficiency and correctness
• Supply the human feedback signals that drive RLHF alignment and improve model safety and helpfulness
• Analyze how models navigate complex chain-of-thought prompts and identify precise failure points
• Benchmark competing model outputs against structured technical taxonomies and quantitative performance criteria

Requirements

• BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative discipline with a machine learning concentration
• Hands-on production experience building, deploying, or fine-tuning machine learning models
• Professional-grade knowledge of neural network architectures such as Transformers, CNNs, and RNNs, plus modern optimization techniques
• Practical experience with prompt engineering, RLHF, or retrieval-augmented generation workflows
• Ability to audit intricate model logic, detect training data contamination, and reason about the mathematical proofs behind ML algorithms
• High attention to detail when spotting hallucinations, biased outputs, or logical errors in AI-generated content

Nice to have

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

Benefits and work setup

• Competitive hourly pay, with researcher-funded tasks often reaching up to $80 per hour
• Fully remote and work-from-home friendly, with no fixed hours
• Quick onboarding: candidates complete a 10-15 minute skills test, then choose tasks that fit their schedule; most tasks fit within a one-hour focused block