AI Engineer/ML Engineer - Senior Developers - AI Training - Jacksonville, US

🏢 Summary: Opportunity for AI and Machine Learning Engineers to train and evaluate large language models through technical reviews, RLHF workflows, and model benchmarking tasks. The role involves auditing ML code, analyzing AI reasoning, and validating model performance using deep learning and MLOps expertise. Participants complete paid remote tasks with flexible hours and compensation up to $80 per hour. 🗂️ Requirements: BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or related quantitative field, Experience building, deploying, or fine-tuning ML models in production, Professional understanding of Transformers, CNNs, RNNs, and optimization techniques, Hands-on experience with Prompt Engineering, RLHF, or RAG workflows, Ability to audit model logic and evaluate ML algorithm proofs, Strong attention to detail in identifying hallucinations, bias, and logical failures, Advanced Python skills, Experience with PyTorch or TensorFlow/Keras 📃 Skills: Python, PyTorch, TensorFlow, Keras, NumPy, Pandas, Scikit-learn, Transformers, HuggingFace, AWS, SageMaker, GoogleCloud, VertexAI, Weights&Biases, LangChain, Pinecone, Milvus, Weaviate, RLHF, RAG, CNNs, RNNs 🏢 Description: About Prolific Prolific is building a large pool of quality human data for AI development. Over 35,000 AI developers, researchers, and organizations use the platform to gather data from paid study participants with diverse experiences and skills. The role We're looking for AI and Machine Learning Engineers to help train and evaluate the next generation of LLMs using deep technical expertise. Candidates complete a short assessment to evaluate suitability for AI tasks. Successful applicants join as participants and get paid to train and evaluate AI models. Researchers looking for these skills tend to pay up to $80 per hour. Tasks may require one hour of uninterrupted work, though many are shorter. What you'll bring Education: - 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 including Transformers, CNNs, and RNNs - Knowledge of optimization techniques LLM Specialization: - Hands-on experience with Prompt Engineering, RLHF (Reinforcement Learning from Human Feedback), or RAG (Retrieval-Augmented Generation) workflows Technical Rigor: - Ability to audit complex model logic - Ability to identify training data contamination - Ability to 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 AI-generated explanations of model architectures, loss functions, and backpropagation for technical accuracy - Validate ML-specific code including training loops, preprocessing scripts, and model evaluations for efficiency and correctness - Provide human feedback to improve model alignment, safety, and helpfulness - Assess AI reasoning on complex chain-of-thought prompts and identify reasoning failures - Conduct comparative testing between model outputs using technical taxonomies and performance metrics Key Technologies Frameworks: - PyTorch - TensorFlow/Keras Language & Data: - Python - NumPy - Pandas - Scikit-learn - Hugging Face Transformers Cloud & MLOps: - AWS SageMaker - Google Cloud Vertex AI - Weights & Biases - LangChain Vector Databases: - Pinecone - Milvus - Weaviate Why join as a participant - Competitive pay rates - Flexible hours - Remote work opportunities - Opportunity to contribute to the development of future AI systems By submitting your application, you agree that Prolific may collect and process personal data for recruiting and organizational planning purposes.

🏢 Summary: Opportunity for AI and Machine Learning Engineers to train and evaluate large language models through technical assessments, code audits, RLHF workflows, and model reasoning analysis. The role involves flexible remote paid tasks focused on improving AI systems using advanced ML and deep learning expertise. 🗂️ Requirements: BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or related quantitative field, Experience building, deploying, or fine-tuning ML models in production, Professional understanding of Transformers, CNNs, RNNs, and optimization techniques, Hands-on experience with Prompt Engineering, RLHF, or RAG workflows, Ability to audit model logic and evaluate ML algorithms, Strong analytical skills for identifying hallucinations, bias, and logical failures, Expert proficiency in PyTorch or TensorFlow/Keras, Advanced Python skills with NumPy, Pandas, and Scikit-learn, Experience with Hugging Face Transformers, Experience with AWS SageMaker, Google Cloud Vertex AI, or MLOps tools, Familiarity with vector databases for RAG evaluation 📃 Skills: Python, PyTorch, TensorFlow, Keras, NumPy, Pandas, Scikit-learn, Transformers, RLHF, RAG, LangChain, SageMaker, VertexAI, AWS, Pinecone, Milvus, Weaviate, CNNs, RNNs, HuggingFace 🏢 Description: About Prolific Prolific is building a large pool of quality human data for AI development. Over 35,000 AI developers, researchers, and organizations use the platform to gather data from paid study participants with a wide variety of experiences, knowledge, and skills. The role We're looking for AI and Machine Learning Engineers to join the Expert Network to help train and evaluate the next generation of LLMs using deep technical expertise. Candidates complete a short assessment to evaluate their suitability for AI tasks. Successful participants are invited to complete paid tasks focused on training and evaluating AI models. Researchers looking for these skills tend to pay up to $80 per hour. Tasks may require one hour of uninterrupted work, though many are shorter. What you'll bring - BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative field with a focus on Machine Learning - Experience building, deploying, or fine-tuning ML models in a production environment - Professional-level understanding of neural network architectures including Transformers, CNNs, and RNNs, along with optimization techniques - Hands-on experience with Prompt Engineering, RLHF (Reinforcement Learning from Human Feedback), or RAG (Retrieval-Augmented Generation) workflows - Ability to audit complex model logic, identify training data contamination, and evaluate mathematical proofs behind ML algorithms - 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 - Review AI-generated explanations of model architectures, loss functions, and backpropagation for technical accuracy - Validate ML-specific code such as training loops, preprocessing scripts, and model evaluations for efficiency and correctness - Provide high-quality human feedback to align models with human intent, safety, and helpfulness - Assess how AI models handle complex chain-of-thought prompts and identify reasoning failures - Conduct comparative testing between different model outputs using technical taxonomies and performance metrics Key Technologies - Expert proficiency in PyTorch or TensorFlow/Keras - Advanced Python with NumPy, Pandas, and Scikit-learn - Experience with Hugging Face Transformers - Experience with AWS SageMaker, Google Cloud Vertex AI, Weights & Biases, and LangChain - Familiarity with Pinecone, Milvus, or Weaviate for RAG evaluation Why join as a Participant - Competitive pay rates - Flexible hours - Remote work opportunities - Opportunity to contribute to the development of future AI systems By submitting an application, candidates agree that personal data may be collected for recruiting and organizational planning purposes as outlined in the Candidate Privacy Notice.

🏢 Summary: Opportunity for AI and Machine Learning Engineers to train and evaluate large language models through technical reviews, RLHF feedback, and model benchmarking tasks. The role involves auditing ML code and reasoning, assessing AI-generated outputs, and contributing expertise in deep learning and LLM workflows on a flexible paid basis. 🗂️ Requirements: BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or related quantitative field, Experience building, deploying, or fine-tuning ML models in production, Professional understanding of Transformers, CNNs, RNNs, and optimization techniques, Hands-on experience with Prompt Engineering, RLHF, or RAG workflows, Ability to audit model logic and evaluate ML algorithms, Strong analytical skills for identifying hallucinations, bias, and logical failures, Proficiency with PyTorch or TensorFlow/Keras, Advanced Python skills with ML libraries, Experience with cloud ML or MLOps platforms, Familiarity with vector databases for RAG evaluation 📃 Skills: Python, PyTorch, TensorFlow, Keras, NumPy, Pandas, Scikit-learn, Transformers, RLHF, RAG, AWS, SageMaker, Vertex, Weights, Biases, LangChain, Pinecone, Milvus, Weaviate, CNNs, RNNs 🏢 Description: About Prolific Prolific is building a large pool of quality human data used by AI developers, researchers, and organizations to gather data from paid study participants. The role We're looking for AI and Machine Learning Engineers to help train and evaluate the next generation of LLMs using deep technical expertise. Candidates complete a short assessment to evaluate suitability for AI tasks. Successful participants are invited to complete paid AI training and evaluation tasks. Researchers looking for these skills tend to pay up to $80 per hour. Tasks may require one hour of uninterrupted work, though many are shorter. What you'll bring • BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative field with a focus on Machine Learning • Experience building, deploying, or fine-tuning ML models in a production environment • Professional-level understanding of neural network architectures including Transformers, CNNs, and RNNs, as well as optimization techniques • Hands-on experience with Prompt Engineering, RLHF (Reinforcement Learning from Human Feedback), or RAG (Retrieval-Augmented Generation) workflows • Ability to audit complex model logic, identify training data contamination, and evaluate mathematical proofs behind ML algorithms • 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 • Review AI-generated explanations of model architectures, loss functions, and backpropagation for technical accuracy • Validate ML-specific code such as training loops, data preprocessing scripts, and model evaluations for efficiency and correctness • Provide high-quality human feedback to align models with human intent, safety, and helpfulness • Assess how AI models navigate complex chain-of-thought prompts and identify reasoning failures • Conduct comparative testing between different model outputs using technical taxonomies and performance metrics Key Technologies • PyTorch or TensorFlow/Keras • Python with NumPy, Pandas, and Scikit-learn • Hugging Face Transformers • AWS SageMaker or Google Cloud Vertex AI • Weights & Biases and LangChain • Pinecone, Milvus, or Weaviate Benefits • Competitive pay rates • Flexible hours • Remote work opportunities • Opportunity to contribute to the development of advanced AI systems Privacy Notice By submitting an application, candidates agree that personal data may be collected for recruiting and organizational planning purposes.

🏢 Summary: Opportunity for AI and Machine Learning Engineers to train and evaluate large language models through technical assessments, model auditing, RLHF workflows, and performance benchmarking. The role involves reviewing AI-generated outputs, validating ML code, and contributing expert human feedback for advanced AI systems. Flexible remote participation with paid AI training tasks and competitive hourly rates. 🗂️ Requirements: BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or related quantitative field, Experience building, deploying, or fine-tuning ML models in production, Professional understanding of Transformers, CNNs, RNNs, and optimization techniques, Hands-on experience with Prompt Engineering, RLHF, or RAG workflows, Ability to audit model logic and evaluate ML algorithms, Strong analytical skills for identifying hallucinations, bias, and logical failures in AI outputs, Expert proficiency in PyTorch or TensorFlow/Keras, Advanced Python skills with NumPy, Pandas, and Scikit-learn, Experience with Hugging Face Transformers, Experience with AWS SageMaker, Google Cloud Vertex AI, or similar MLOps tools, Familiarity with Pinecone, Milvus, or Weaviate 📃 Skills: Python, PyTorch, TensorFlow, Keras, NumPy, Pandas, Scikit-learn, Transformers, CNNs, RNNs, RLHF, RAG, LangChain, AWS, SageMaker, GoogleCloud, VertexAI, Pinecone, Milvus, Weaviate, HuggingFace 🏢 Description: About Prolific Prolific is building a large pool of quality human data for AI development. Over 35,000 AI developers, researchers, and organizations use the platform to gather data from paid study participants. The role We're looking for AI and Machine Learning Engineers to help train and evaluate the next generation of LLMs using deep technical expertise. Candidates complete a short assessment to evaluate suitability for AI tasks. Successful participants are invited to join the platform and get paid to train and evaluate AI models. Researchers looking for these skills tend to pay up to $80 per hour. Tasks may require one hour of uninterrupted work, though many are shorter. What you'll bring - BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative field with a focus on Machine Learning - Experience building, deploying, or fine-tuning ML models in a production environment - Professional-level understanding of neural network architectures including Transformers, CNNs, and RNNs, as well as optimization techniques - Hands-on experience with Prompt Engineering, RLHF (Reinforcement Learning from Human Feedback), or RAG (Retrieval-Augmented Generation) workflows - Ability to audit complex model logic, identify training data contamination, and evaluate mathematical proofs behind ML algorithms - 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 - Review AI-generated explanations of model architectures, loss functions, and backpropagation for technical accuracy - Validate ML-specific code such as training loops, preprocessing scripts, and model evaluations for efficiency and correctness - Provide high-quality human feedback to improve alignment, safety, and helpfulness of AI models - Assess AI reasoning on complex chain-of-thought prompts and identify reasoning failures - Conduct comparative testing of model outputs using technical taxonomies and performance metrics Key Technologies - Expert proficiency in PyTorch or TensorFlow/Keras - Advanced Python including NumPy, Pandas, and Scikit-learn - Experience with Hugging Face Transformers - Experience with AWS SageMaker, Google Cloud Vertex AI, Weights & Biases, and LangChain - Familiarity with Pinecone, Milvus, or Weaviate for RAG evaluation Benefits - Competitive pay rates up to $80 per hour - Flexible working hours - Remote work opportunities - Opportunity to contribute to next-generation AI systems Privacy Notice By submitting an application, candidates agree that personal data may be collected for recruiting and organizational planning purposes.

🏢 Summary: Opportunity to join an expert network evaluating and training next-generation LLMs through technical reviews, RLHF feedback, and ML benchmarking tasks. The role involves auditing AI-generated code and model reasoning while working flexibly on paid AI training assignments. Candidates need strong machine learning, deep learning, and LLM expertise with hands-on experience in production ML environments. 🗂️ Requirements: BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or related quantitative field, Experience building, deploying, or fine-tuning ML models in production, Professional understanding of Transformers, CNNs, RNNs, and optimization techniques, Hands-on experience with Prompt Engineering, RLHF, or RAG workflows, Ability to audit model logic and evaluate ML algorithms, Strong attention to detail in identifying hallucinations, bias, and logical failures, Advanced Python and ML framework proficiency, Experience with cloud ML or MLOps platforms 📃 Skills: Python, PyTorch, TensorFlow, Keras, NumPy, Pandas, Scikit-learn, Transformers, RLHF, RAG, LangChain, SageMaker, Vertex, AWS, GCP, Pinecone, Milvus, Weaviate, Weights, Biases, CNNs, RNNs 🏢 Description: About Prolific Prolific is building a global pool of quality human data for AI development. Over 35,000 AI developers, researchers, and organizations use the platform to gather data from paid study participants with diverse experiences and skills. The role We're looking for AI and Machine Learning Engineers to help train and evaluate the next generation of LLMs using deep technical expertise. Candidates complete a short assessment to evaluate their suitability for AI tasks. Successful participants will join the platform to complete paid AI training and evaluation tasks. Researchers looking for these skills tend to pay up to $80 per hour. Tasks may require one hour of uninterrupted work, though many are shorter. What you'll bring - BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative field with a focus on Machine Learning - Experience building, deploying, or fine-tuning ML models in a production environment - Professional-level understanding of neural network architectures including Transformers, CNNs, and RNNs, as well as optimization techniques - Hands-on experience with Prompt Engineering, RLHF (Reinforcement Learning from Human Feedback), or RAG (Retrieval-Augmented Generation) workflows - Ability to audit complex model logic, identify training data contamination, and evaluate mathematical proofs behind ML algorithms - 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 - Review AI-generated explanations of model architectures, loss functions, and backpropagation for technical accuracy - Validate ML-specific code such as training loops, data preprocessing scripts, and model evaluations for efficiency and correctness - Provide high-quality human feedback to align models with human intent, safety, and helpfulness - Assess how AI models navigate complex chain-of-thought prompts and identify reasoning failures - Conduct comparative testing between different model outputs using technical taxonomies and performance metrics Key Technologies - Expert proficiency in PyTorch or TensorFlow/Keras - Advanced Python with NumPy, Pandas, and Scikit-learn - Experience with Hugging Face Transformers - Experience with AWS SageMaker, Google Cloud Vertex AI, Weights & Biases, and LangChain - Familiarity with Pinecone, Milvus, or Weaviate for RAG evaluation Benefits - Competitive pay rates up to $80 per hour - Flexible working hours - Remote work opportunities - Ability to contribute to the development of advanced AI systems Privacy Notice By submitting an application, candidates agree that personal data may be collected for recruiting and organizational planning purposes.

🏢 Summary: Paid, flexible opportunity for AI and Machine Learning Engineers to train and evaluate advanced LLMs by reviewing model logic, auditing ML code, and providing expert feedback for RLHF and RAG workflows. Participants complete technical assessment tasks and contribute high‑quality human feedback to improve model accuracy, reasoning, and alignment. 🗂️ Requirements: BS/MS/PhD in Computer Science, Artificial Intelligence, Robotics or related quantitative field, Experience building, deploying or fine-tuning ML models in production, Professional knowledge of neural network architectures (Transformers, CNNs, RNNs), Hands-on experience with Prompt Engineering, RLHF or RAG, Proficiency in PyTorch or TensorFlow/Keras, Advanced Python programming, Ability to audit ML code, model logic and training data quality 📃 Skills: Python, PyTorch, TensorFlow, Keras, NumPy, Pandas, Scikit-learn, Transformers, HuggingFace, AWS, SageMaker, GCP, VertexAI, Weights&Biases, LangChain, Pinecone, Milvus, Weaviate, RLHF, RAG, CNN, RNN 🏢 Description: AI & Machine Learning Engineer - AI Training About Prolific Prolific is not just another player in the AI space – we are building the biggest pool of quality human data in the world. Over 35,000 AI developers, researchers, and organizations use Prolific to gather data from paid study participants with a wide variety of experiences, knowledge, and skills. The role We're looking for AI and Machine Learning Engineers to join our Expert Network to help train and evaluate the next generation of LLMs using deep technical expertise. If you have the necessary experience, we'll send you a quick 10- to 15-minute test to assess your skills and suitability for AI tasks. If successful, you'll be invited to join Prolific as a participant, where you'll get paid to train and evaluate powerful AI models. Researchers looking for your skills tend to pay up to $80 per hour. You must be prepared to complete paid tasks that require one hour of uninterrupted work, though many are shorter. 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 the 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 (CoT) 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. Why Prolific is a great platform to join as a Participant Joining our Expert Network will give you the chance to influence the AI models of the future using professional legal expertise. Once you pass our assessment, you can join Prolific in just 15 minutes, and start enjoying competitive pay rates, flexible hours, and the ability to work from home. We've built a unique platform that connects researchers and companies with a global pool of participants, enabling the collection of high-quality, ethically sourced human behavioural data and feedback. This data is the cornerstone of developing more accurate, nuanced, and aligned AI systems. We believe that the next leap in AI capabilities won't come solely from scaling existing models, but from integrating diverse human perspectives and behaviours into AI development. By providing this crucial human data infrastructure, Prolific is positioning itself at the forefront of the next wave of AI innovation – one that reflects the breadth and the best of humanity. Links to more information on Prolific Website Youtube Privacy Statement By submitting your application, you agree that Prolific may collect your personal data for recruiting and global organisation planning. Prolific's Candidate Privacy Notice explains what personal information Prolific may process, where Prolific may process your personal information, its purposes for processing your personal information, and the rights you can exercise over Prolific use of your personal personal information.

🏢 Summary: Opportunity to join an expert network to train and evaluate advanced large language models by reviewing architectures, auditing ML code, and refining RLHF workflows. The role involves applying deep machine learning expertise to assess model reasoning, benchmark performance, and improve alignment. Work is task-based and remote, focused on high-level technical evaluation of AI systems. 🗂️ Requirements: BS/MS/PhD in Computer Science, AI, Robotics, or related quantitative field, Professional experience building, deploying, or fine-tuning ML models in production, Strong knowledge of neural networks (Transformers, CNNs, RNNs) and optimization techniques, Hands-on experience with Prompt Engineering, RLHF, or RAG workflows, Ability to audit ML code, training loops, and data preprocessing pipelines, Experience evaluating model reasoning, hallucinations, and biased outputs, Proficiency in PyTorch or TensorFlow/Keras, Advanced Python for ML and data analysis 📃 Skills: Python, PyTorch, TensorFlow, Keras, NumPy, Pandas, Scikit-learn, Transformers, HuggingFace, RLHF, RAG, LangChain, AWS, SageMaker, GCP, VertexAI, Weights&Biases, Pinecone, Milvus, Weaviate, CNN, RNN 🏢 Description: AI & Machine Learning Engineer - AI Training About Prolific Prolific is not just another player in the AI space – we are building the biggest pool of quality human data in the world. Over 35,000 AI developers, researchers, and organizations use Prolific to gather data from paid study participants with a wide variety of experiences, knowledge, and skills. The role We're looking for AI and Machine Learning Engineers to join our Expert Network to help train and evaluate the next generation of LLMs using deep technical expertise. If you have the necessary experience, we'll send you a quick 10- to 15-minute test to assess your skills and suitability for AI tasks. If successful, you'll be invited to join Prolific as a participant, where you'll get paid to train and evaluate powerful AI models. Researchers looking for your skills tend to pay up to $80 per hour. You must be prepared to complete paid tasks that require one hour of uninterrupted work, though many are shorter. 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 the 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 (CoT) 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. Why Prolific is a great platform to join as a Participant Joining our Expert Network will give you the chance to influence the AI models of the future using professional legal expertise. Once you pass our assessment, you can join Prolific in just 15 minutes, and start enjoying competitive pay rates, flexible hours, and the ability to work from home. We've built a unique platform that connects researchers and companies with a global pool of participants, enabling the collection of high-quality, ethically sourced human behavioural data and feedback. This data is the cornerstone of developing more accurate, nuanced, and aligned AI systems. We believe that the next leap in AI capabilities won't come solely from scaling existing models, but from integrating diverse human perspectives and behaviours into AI development. By providing this crucial human data infrastructure, Prolific is positioning itself at the forefront of the next wave of AI innovation – one that reflects the breadth and the best of humanity. Links to more information on Prolific Website Youtube Privacy Statement By submitting your application, you agree that Prolific may collect your personal data for recruiting and global organisation planning. Prolific's Candidate Privacy Notice explains what personal information Prolific may process, where Prolific may process your personal information, its purposes for processing your personal information, and the rights you can exercise over Prolific use of your personal personal information.

🏢 Summary: Opportunity to join an expert network as an AI & Machine Learning Engineer to train, evaluate, and improve large language models through technical audits, RLHF refinement, and benchmarking tasks. The role involves reviewing model architectures, validating ML code, and assessing reasoning quality using deep learning expertise. It is a paid, flexible engagement focused on advancing next-generation AI systems. 🗂️ Requirements: BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or related quantitative field, Experience building, deploying, or fine-tuning ML models in production, Professional knowledge of neural network architectures (Transformers, CNNs, RNNs), Hands-on experience with Prompt Engineering, RLHF, or RAG workflows, Ability to audit model logic and detect training data contamination, Experience evaluating ML algorithms and mathematical foundations, Proficiency in PyTorch or TensorFlow/Keras, Advanced Python for ML and data processing, Experience with cloud ML platforms (AWS SageMaker or Google Vertex AI), Familiarity with vector databases for RAG systems 📃 Skills: Python, PyTorch, TensorFlow, Keras, NumPy, Pandas, Scikit-learn, Transformers, HuggingFace, AWS, SageMaker, GCP, VertexAI, LangChain, Weights&Biases, Pinecone, Milvus, Weaviate, RLHF, RAG, CNN, RNN 🏢 Description: AI & Machine Learning Engineer - AI Training About Prolific Prolific is not just another player in the AI space – we are building the biggest pool of quality human data in the world. Over 35,000 AI developers, researchers, and organizations use Prolific to gather data from paid study participants with a wide variety of experiences, knowledge, and skills. The role We're looking for AI and Machine Learning Engineers to join our Expert Network to help train and evaluate the next generation of LLMs using deep technical expertise. If you have the necessary experience, we'll send you a quick 10- to 15-minute test to assess your skills and suitability for AI tasks. If successful, you'll be invited to join Prolific as a participant, where you'll get paid to train and evaluate powerful AI models. Researchers looking for your skills tend to pay up to $80 per hour. You must be prepared to complete paid tasks that require one hour of uninterrupted work, though many are shorter. 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 the 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 (CoT) 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. Why Prolific is a great platform to join as a Participant Joining our Expert Network will give you the chance to influence the AI models of the future using professional legal expertise. Once you pass our assessment, you can join Prolific in just 15 minutes, and start enjoying competitive pay rates, flexible hours, and the ability to work from home. We've built a unique platform that connects researchers and companies with a global pool of participants, enabling the collection of high-quality, ethically sourced human behavioural data and feedback. This data is the cornerstone of developing more accurate, nuanced, and aligned AI systems. We believe that the next leap in AI capabilities won't come solely from scaling existing models, but from integrating diverse human perspectives and behaviours into AI development. By providing this crucial human data infrastructure, Prolific is positioning itself at the forefront of the next wave of AI innovation – one that reflects the breadth and the best of humanity. Links to more information on Prolific Website Youtube Privacy Statement By submitting your application, you agree that Prolific may collect your personal data for recruiting and global organisation planning. Prolific's Candidate Privacy Notice explains what personal information Prolific may process, where Prolific may process your personal information, its purposes for processing your personal information, and the rights you can exercise over Prolific use of your personal personal information.

🏢 Summary: Opportunity to join an expert network to train and evaluate advanced large language models by auditing architectures, reviewing ML code, refining RLHF workflows, and benchmarking model performance. The role involves paid, task-based work requiring deep machine learning expertise and technical evaluation of AI systems. 🗂️ Requirements: BS/MS/PhD in Computer Science, AI, Robotics or related quantitative field, Experience building, deploying or fine-tuning ML models in production, Professional knowledge of neural network architectures (Transformers, CNNs, RNNs), Hands-on experience with Prompt Engineering, RLHF or RAG workflows, Ability to audit model logic and evaluate ML algorithms mathematically, Experience reviewing ML code, training loops and data preprocessing, Proficiency in PyTorch or TensorFlow/Keras, Advanced Python for ML and data processing, Experience with cloud ML platforms (AWS SageMaker or Google Vertex AI), Familiarity with vector databases for RAG evaluation 📃 Skills: Python, PyTorch, TensorFlow, Keras, NumPy, Pandas, Scikit-learn, Transformers, HuggingFace, RLHF, RAG, LangChain, AWS, SageMaker, GCP, VertexAI, Weights&Biases, Pinecone, Milvus, Weaviate, CNN, RNN 🏢 Description: AI & Machine Learning Engineer - AI Training About Prolific Prolific is not just another player in the AI space – we are building the biggest pool of quality human data in the world. Over 35,000 AI developers, researchers, and organizations use Prolific to gather data from paid study participants with a wide variety of experiences, knowledge, and skills. The role We're looking for AI and Machine Learning Engineers to join our Expert Network to help train and evaluate the next generation of LLMs using deep technical expertise. If you have the necessary experience, we'll send you a quick 10- to 15-minute test to assess your skills and suitability for AI tasks. If successful, you'll be invited to join Prolific as a participant, where you'll get paid to train and evaluate powerful AI models. Researchers looking for your skills tend to pay up to $80 per hour. You must be prepared to complete paid tasks that require one hour of uninterrupted work, though many are shorter. 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 the 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 (CoT) 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. Why Prolific is a great platform to join as a Participant Joining our Expert Network will give you the chance to influence the AI models of the future using professional legal expertise. Once you pass our assessment, you can join Prolific in just 15 minutes, and start enjoying competitive pay rates, flexible hours, and the ability to work from home. We've built a unique platform that connects researchers and companies with a global pool of participants, enabling the collection of high-quality, ethically sourced human behavioural data and feedback. This data is the cornerstone of developing more accurate, nuanced, and aligned AI systems. We believe that the next leap in AI capabilities won't come solely from scaling existing models, but from integrating diverse human perspectives and behaviours into AI development. By providing this crucial human data infrastructure, Prolific is positioning itself at the forefront of the next wave of AI innovation – one that reflects the breadth and the best of humanity. Links to more information on Prolific Website Youtube Privacy Statement By submitting your application, you agree that Prolific may collect your personal data for recruiting and global organisation planning. Prolific's Candidate Privacy Notice explains what personal information Prolific may process, where Prolific may process your personal information, its purposes for processing your personal information, and the rights you can exercise over Prolific use of your personal personal information.

🏢 Summary: Paid opportunity to train and evaluate advanced large language models by auditing model logic, reviewing ML code, and refining RLHF workflows. The role involves applying deep machine learning expertise to assess, benchmark, and improve AI systems. Work is task-based with flexible hours and competitive hourly compensation. 🗂️ Requirements: BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or related quantitative field, Experience building, deploying, or fine-tuning ML models in production, Professional understanding of neural network architectures (Transformers, CNNs, RNNs), Hands-on experience with Prompt Engineering, RLHF, or RAG workflows, Ability to audit model logic and evaluate mathematical foundations of ML algorithms, Experience reviewing ML code, training loops, and data preprocessing pipelines, Proficiency in PyTorch or TensorFlow/Keras, Advanced Python programming for ML tasks, Experience with cloud ML platforms (AWS SageMaker or Google Vertex AI), Familiarity with vector databases for RAG systems 📃 Skills: Python, PyTorch, TensorFlow, Keras, NumPy, Pandas, Scikit-learn, Transformers, HuggingFace, AWS, SageMaker, GoogleCloud, VertexAI, Weights&Biases, LangChain, Pinecone, Milvus, Weaviate, RLHF, RAG, CNN, RNN 🏢 Description: AI & Machine Learning Engineer - AI Training About Prolific Prolific is not just another player in the AI space – we are building the biggest pool of quality human data in the world. Over 35,000 AI developers, researchers, and organizations use Prolific to gather data from paid study participants with a wide variety of experiences, knowledge, and skills. The role We're looking for AI and Machine Learning Engineers to join our Expert Network to help train and evaluate the next generation of LLMs using deep technical expertise. If you have the necessary experience, we'll send you a quick 10- to 15-minute test to assess your skills and suitability for AI tasks. If successful, you'll be invited to join Prolific as a participant, where you'll get paid to train and evaluate powerful AI models. Researchers looking for your skills tend to pay up to $80 per hour. You must be prepared to complete paid tasks that require one hour of uninterrupted work, though many are shorter. 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 the 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 (CoT) 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. Why Prolific is a great platform to join as a Participant Joining our Expert Network will give you the chance to influence the AI models of the future using professional legal expertise. Once you pass our assessment, you can join Prolific in just 15 minutes, and start enjoying competitive pay rates, flexible hours, and the ability to work from home. We've built a unique platform that connects researchers and companies with a global pool of participants, enabling the collection of high-quality, ethically sourced human behavioural data and feedback. This data is the cornerstone of developing more accurate, nuanced, and aligned AI systems. We believe that the next leap in AI capabilities won't come solely from scaling existing models, but from integrating diverse human perspectives and behaviours into AI development. By providing this crucial human data infrastructure, Prolific is positioning itself at the forefront of the next wave of AI innovation – one that reflects the breadth and the best of humanity. Links to more information on Prolific Website Youtube Privacy Statement By submitting your application, you agree that Prolific may collect your personal data for recruiting and global organisation planning. Prolific's Candidate Privacy Notice explains what personal information Prolific may process, where Prolific may process your personal information, its purposes for processing your personal information, and the rights you can exercise over Prolific use of your personal personal information.