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

prolificacademicltd; USA; Remote; USD 66.000 - 110.000; Teilzeit

Benefits dieser Stelle

Competitive pay

Remote-friendly

Flexible schedule

Fast onboarding

Zusammenfassung

prolificacademicltd seeks experienced AI/ML Engineers for a remote, asynchronous contributor network. You will tackle paid technical tasks drawing on real-world ML production experience to train and rigorously evaluate large language models, with most sessions fitting within an hour.

Responsibilities include evaluating architecture explanations, inspecting training pipelines, and providing high-quality RLHF feedback to improve model alignment.

Qualifikationen

• Degree (BS, MS or PhD) in CS, AI, robotics or related quantitative field with ML emphasis.
• Proven experience building, deploying or fine-tuning ML models in production.
• Deep expertise in neural networks (Transformers, CNNs, RNNs) and modern optimization methods.
• Practical experience with prompt engineering, RLHF or retrieval-augmented generation workflows.
• Strong rigor auditing model logic and training data quality; detecting contamination.
• Meticulous attention to detail to identify hallucinations or biased outputs.

Aufgaben

• Evaluate AI-produced explanations of architectures, loss functions and backpropagation for technical soundness.
• Inspect ML code/notebooks, training pipelines and evaluation scripts for efficiency and correctness.
• Provide high-quality human feedback used in RLHF training to improve alignment with intent.
• Trace chain-of-thought reasoning and flag points where model logic collapses.
• Design and execute benchmark tests comparing model outputs against defined metrics.

Kenntnisse

ML production

Neural networks

RLHF / prompts

Model evaluation

Code review

Data quality analysis

Ausbildung

BS/MS/PhD in CS/AI/Robotics or ML

Tools

PyTorch

TensorFlow/Keras

NumPy/Pandas

Scikit-learn

Hugging Face

Jobbeschreibung

Role overview

An expert contributor network is recruiting experienced AI and Machine Learning Engineers to help train and rigorously evaluate large language models. Contributors receive paid technical tasks that leverage real-world ML production experience, with most sessions designed to fit within an hour of focused work. The arrangement is fully remote, asynchronous, and built to accommodate practicing engineers.

Responsibilities

• Evaluate AI-produced explanations of model architectures, loss functions, and backpropagation for technical soundness
• Inspect ML code and notebooks, including training pipelines, preprocessing logic, and evaluation scripts, for efficiency and correctness
• Provide high-quality human feedback used in RLHF training to improve model alignment with human intent
• Trace chain-of-thought reasoning and flag specific points where model logic collapses
• Design and execute comparative benchmark tests across model outputs against defined taxonomies and metrics

Requirements

• Degree (BS, MS, or PhD) in Computer Science, Artificial Intelligence, Robotics, or another quantitative discipline with a machine learning emphasis
• Demonstrated experience building, deploying, or fine-tuning ML models in production settings
• Deep expertise in neural network architectures (Transformers, CNNs, RNNs) and contemporary optimization methods
• Practical experience with prompt engineering, RLHF, or retrieval-augmented generation workflows
• Strong technical rigor when auditing model logic, spotting training data contamination, and evaluating mathematical foundations of algorithms
• Meticulous attention to detail for identifying hallucinations, biased outputs, or reasoning failures

Nice to have

• Expert-level command of PyTorch or TensorFlow/Keras
• Advanced Python fluency with NumPy, Pandas, and Scikit-learn, plus Hugging Face Transformers experience
• Cloud and MLOps exposure with AWS SageMaker, Google Cloud Vertex AI, Weights & Biases, or LangChain
• Familiarity with vector stores such as Pinecone, Milvus, or Weaviate for evaluating RAG pipelines

Benefits and work setup

• Competitive hourly rates, with researcher-driven tasks typically paying up to $80 per hour
• Remote-first, flexible schedule; contributors log in and pick up tasks as available
• Lightweight onboarding: a 10-15 minute assessment unlocks the contributor pool, with most tasks capped at roughly one hour of uninterrupted work