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Ai Human Feedback Jobs (NOW HIRING)

Your role will involve designing and implementing advanced systems that align human feedback into AI training processes, such as Reinforcement Learning from Human Feedback (RLHF), Direct Preference ...

Develop post-editing and quality assurance tools to augment human translators, incorporating human-in-the-loop feedback. * Work closely with linguists, product managers, and engineers to integrate AI ...

Develop post-editing and quality assurance tools to augment human translators, incorporating human-in-the-loop feedback. * Work closely with linguists, product managers, and engineers to integrate AI ...

... human feedback, and iterative task refinement • Develop infrastructure for prompt orchestration, memory, retrieval, and contextual personalization • Build internal tools and user-facing product ...

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Ai Human Feedback information

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$26.5K

$44.2K

$60K

How much do ai human feedback jobs pay per year?

As of Aug 8, 2026, the average yearly pay for ai human feedback in the United States is $44,245.00, according to ZipRecruiter salary data. Most workers in this role earn between $37,500.00 and $48,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an AI human feedback specialist?

To thrive as an AI Human Feedback Specialist, you need a solid understanding of language, critical thinking, and attention to detail, often supported by a background in linguistics, communication, or a related field. Familiarity with annotation tools, feedback platforms, and sometimes basic programming or data analysis skills is typically required. Strong written communication, analytical thinking, and the ability to provide constructive, unbiased feedback are essential soft skills. These competencies ensure the accuracy, fairness, and effectiveness of AI systems by enabling clear and high-quality human evaluation.

What is an AI human feedback?

An AI Human Feedback job involves evaluating and providing feedback on the outputs generated by artificial intelligence models. People in these roles assess the accuracy, relevance, and appropriateness of AI responses, helping improve their performance and reliability. This work is essential for training, testing, and refining AI systems to ensure they align with human expectations and ethical guidelines. AI Human Feedback roles can include tasks like rating chatbot answers, annotating data, and identifying biases or errors in AI-generated content.

What is the difference between Ai Human Feedback vs Data Annotator?

AspectAi Human FeedbackData Annotator
Required CredentialsBasic technical skills, sometimes certifications in AI or data labelingMinimal formal education, training often provided on the job
Work EnvironmentRemote or office-based, collaborative with AI teamsPrimarily remote or on-site data labeling tasks
Industry UsageAI development, machine learning projectsData preparation for AI, machine learning, and analytics
Search & Comparison IntentUnderstanding roles in AI feedback processesData labeling and annotation tasks for AI training

Ai Human Feedback involves providing insights to improve AI models, often requiring some technical understanding. Data Annotators focus on labeling data to train AI systems, typically with minimal formal credentials. Both roles are essential in AI development but differ in scope and technical requirements.

What are some typical challenges faced by professionals in AI human feedback roles and how can they be addressed?

Professionals in AI human feedback roles often encounter challenges such as ensuring consistency and objectivity when evaluating AI outputs, managing large volumes of data, and staying updated with evolving AI technologies. It’s important to develop clear guidelines and maintain open communication with team members to align on evaluation standards. Regular training sessions and peer reviews can help enhance accuracy and address potential biases, fostering a collaborative and supportive work environment.
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What cities are hiring for Ai Human Feedback jobs? Cities with the most Ai Human Feedback job openings:
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Infographic showing various Ai Human Feedback job openings in the United States as of August 2026, with employment types broken down into 83% Full Time, 15% Part Time, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $44,245 per year, or $21.3 per hour.

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

Prolific

Remote

$107K - $146K/yr

Full-time

Re-posted 6 days ago


Job description

Job Summary:
Prolific is building the biggest pool of quality human data in the world, and they are seeking AI and Machine Learning Engineers to join their Expert Network. The role involves training and evaluating AI models, ensuring technical accuracy, and providing high-quality human feedback to align models with human intent.
Responsibilities:
• 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.
Qualifications:
Required:
• 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.
• 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.
Company:
Building the most advanced global infrastructure for People Science. Founded in 2014, the company is headquartered in London, GBR, with a team of 51-200 employees. The company is currently Growth Stage.