AI Engineer/ML Engineer - Senior Developers - AI Training - Colchester, UK

prolificacademicltd; United States; Remote; USD 83,000 - 110,000; Full time

Benefits offered by this job

Fully remote

Flexible hours

Skills assessment

Job summary

Prolific Academic Ltd is seeking Senior AI/ML Engineers to join a network that trains and evaluates large language models. Work is entirely remote and paid per task, offering flexible hours and short assignments that require deep technical expertise rather than full-time employment.

The role focuses on auditing model logic, RLHF refinement, and analytical benchmarking, with compensation reaching up to $80 per hour for in-demand expertise.

Qualifications

• BS/MS/PhD in Computer Science, AI, Robotics, or related quantitative field with ML focus.
• Hands-on experience building, deploying, or fine-tuning ML models in production.
• Understanding of neural networks (Transformers, CNNs, RNNs) and modern optimization techniques.
• Experience with prompt engineering, RLHF, or retrieval-augmented generation workflows.
• Ability to audit complex model logic and detect data contamination.

Responsibilities

• Review explanations of model architectures, loss functions, and backpropagation for accuracy.
• Audit ML code and notebooks for correctness and efficiency.
• Provide feedback to refine RLHF and align model behavior with safety and usefulness.
• Analyse model reasoning and identify where chain-of-thought prompts fail.
• Run benchmark tests across model outputs using defined metrics.

Skills

ML Fundamentals

Prompt Engineering

RLHF

Code Review

Education

PhD in AI/CS

MS in ML/AI

Tools

PyTorch

TensorFlow/Keras

AWS SageMaker

LangChain

Weights & Biases

Pinecone

Job description

Role overview

Senior AI and Machine Learning Engineers are invited to join an expert contributor network that trains and evaluates large language models on behalf of AI researchers and developers. Contributors are paid per task, work remotely on a flexible schedule, and focus on short, focused assignments that draw on deep technical expertise rather than full-time employment.

Responsibilities

• Review AI-generated explanations of model architectures, loss functions, and backpropagation for technical accuracy.
• Audit machine learning code and notebooks, including training loops, preprocessing scripts, and evaluation pipelines, for correctness and efficiency.
• Provide structured human feedback used to refine RLHF frameworks and align model behaviour with human intent, safety, and helpfulness.
• Analyse how models navigate complex chain-of-thought prompts and pinpoint where reasoning breaks down.
• Run comparative benchmark tests across model outputs using defined taxonomies and performance metrics.

Requirements

• BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative field with a focus on Machine Learning.
• Hands-on experience building, deploying, or fine-tuning machine learning models in a production environment.
• Professional-level understanding of neural network architectures such as Transformers, CNNs, and RNNs, plus modern optimisation techniques.
• Practical experience with prompt engineering, RLHF, or retrieval-augmented generation workflows.
• Ability to audit complex model logic, detect training data contamination, and evaluate mathematical proofs behind ML algorithms.
• High attention to detail when spotting hallucinations, biased outputs, or logical failures in AI-generated technical content.

Nice to have

• Expert proficiency in PyTorch or TensorFlow/Keras.
• Advanced Python skills (NumPy, Pandas, Scikit-learn) and familiarity with Hugging Face Transformers.
• Exposure to AWS SageMaker, Google Cloud Vertex AI, or tools such as Weights and Biases and LangChain.
• Familiarity with vector databases like Pinecone, Milvus, or Weaviate for RAG evaluation.

Benefits and work setup

• Paid contributor model with rates reaching up to $80 per hour for in-demand expertise.
• Flexible hours and fully remote participation, with many tasks shorter than one hour of uninterrupted work.
• Onboarding begins with a 10–15 minute skills assessment before joining the network.