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# AI Engineer/ML Engineer - Senior Developers - AI Training - UK

- **Company:** Prolific
- **Location:** Greater London, UK (Remote available)
- **Experience:** Expert
- **Salary:** £166,400.0
- **Contract:** Permanent contract
- **Skills:** Training Data, Artificial Intelligence, Artificial Neural Networks, High-Level Architecture, Machine Learning, Large Language Models, Prompt Engineering, Deep Learning, Information Technology, Optimization Algorithms
- **Published:** September 8, 2026
- **Apply:** https://www.collegerecruiter.com/job/2840614335-ai-engineerml-engineer-senior-developers-ai-training-uk

## About the Role

* Education: a BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative field with a focus on Machine Learning.
 * Professional Experience: experience building, deploying, or fine-tuning ML models in a production environment.
 * Deep Learning Mastery: professional-level understanding of neural network architectures (Transformers, CNNs, RNNs) and optimization techniques.
 * LLM Specialization: hands-on experience with Prompt Engineering, RLHF, or RAG workflows.
 * Technical Rigor: ability to audit complex model logic, identify training data contamination, and evaluate mathematical proofs behind ML algorithms.
 * Analytical Critique: high attention to detail in spotting hallucinations, biased outputs, or logical failures in AI-generated technical content.

## Description

* Evaluate LLM architecture logic: review AI-generated explanations of model architectures, loss functions, and backpropagation for technical accuracy.
 * Audit code & notebooks: validate ML-specific code (training loops, data preprocessing scripts, model evaluations) for efficiency and correctness.
 * Refine RLHF frameworks: provide high-quality human feedback to align models with human intent, safety, and usefulness.
 * Analyze model reasoning: assess how an AI model navigates complex chain-of-thought prompts and identify where reasoning breaks down.
 * Benchmark performance: conduct comparative testing between different model outputs based on technical taxonomies and performance metrics.


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