AI & Machine Learning Engineer - AI Training
Prolific is building the biggest pool of quality human data in the world, and we need AI and Machine Learning Engineers to help train and evaluate the next generation of LLMs. If you have the necessary experience, we’ll administer a short test to assess your skills and suitability for AI tasks. Successful candidates will be invited to join Prolific as participants, where they’ll get paid for training and evaluating powerful AI models.
What you’ll bring
* 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 (Reinforcement Learning from Human Feedback), or RAG (Retrieval‑Augmented Generation) workflows.
* Technical Rigor: the 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.
What you’ll be doing in the role
* 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 (e.g., training loops, data preprocessing scripts, or model evaluations) for efficiency and correctness.
* Refine RLHF Frameworks: provide high‑quality human feedback necessary to align models with human intent, safety, and helpfulness.
* Analyze Model Reasoning: critically assess how an AI model navigates complex chain‑of‑thought prompts and identify where the reasoning breaks down.
* Benchmark Performance: conduct comparative testing between different model outputs based on specific technical taxonomies and performance metrics.
Key Technologies
* Frameworks: expert proficiency in PyTorch or TensorFlow/Keras.
* Language & Data: advanced Python (NumPy, Pandas, Scikit‑learn) and experience with Hugging Face Transformers.
* Cloud & MLOps: experience with AWS (SageMaker), Google Cloud (Vertex AI), or specialized tools like Weights & Biases and LangChain.
* Vector Databases: familiarity with Pinecone, Milvus, or Weaviate for RAG evaluation.
Benefits
Competitive pay rates (up to $80 per hour), flexible hours, ability to work from home, and the opportunity to influence AI models through professional feedback.
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