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

prolificacademicltd; United States; Remote; USD 34,000 - 110,000; Part time

Job summary

prolificacademicltd seeks contributors to an expert network of machine learning professionals to train and evaluate large language models. This is a paid, contract role in Austin with a flexible remote schedule; tasks involve reviewing explanations, auditing code, and providing structured feedback to RLHF workflows.

You will work on prompts, model reasoning, and performance metrics, with hourly compensation; a brief skills assessment starts onboarding once accepted and studies are available.

Qualifications

• BS, MS, or PhD in Computer Science, AI, Robotics, or a related quantitative field with a machine learning focus.
• Hands-on production experience building, deploying, or fine-tuning ML models.
• Professional-level understanding of neural network architectures such as Transformers, CNNs, and RNNs, plus optimization techniques.
• Practical experience with prompt engineering, RLHF, or retrieval-augmented generation (RAG) workflows.
• Ability to audit model logic, flag potential training-data contamination, and evaluate the mathematical proofs behind ML algorithms.
• Strong attention to detail for spotting hallucinations, biased outputs, or logical failures in AI-generated technical content.

Responsibilities

• Review AI-generated explanations of model architectures, loss functions, and backpropagation for technical correctness.
• Audit machine learning code, training loops, preprocessing scripts, and evaluation notebooks for efficiency and accuracy.
• Provide structured human feedback that supports reinforcement learning from human feedback (RLHF) workflows and model alignment.
• Critically assess how models navigate complex chain-of-thought prompts and pinpoint where reasoning breaks down.
• Benchmark and compare model outputs against defined technical taxonomies and performance metrics.

Skills

ML model deployment

Neural networks

Prompt engineering

RLHF

Code auditing

Attention to detail

Education

BS in Computer Science

MS in AI/ML

PhD in related field

Tools

PyTorch

TensorFlow

Keras

AWS SageMaker

Vertex AI

Weights & Biases

LangChain

Pinecone

Milvus

Weaviate

Job description

Role overview

Join an expert network of machine learning professionals contributing to the training and evaluation of large language models. This is a participant-based, paid contract role in Austin where your technical expertise directly shapes AI model accuracy, safety, and reasoning quality. Successful candidates complete paid study tasks, typically requiring up to one hour of focused work, on a flexible remote schedule.

Responsibilities

• Review AI-generated explanations of model architectures, loss functions, and backpropagation for technical correctness
• Audit machine learning code, training loops, preprocessing scripts, and evaluation notebooks for efficiency and accuracy
• Provide structured human feedback that supports reinforcement learning from human feedback (RLHF) workflows and model alignment
• Critically assess how models navigate complex chain-of-thought prompts and pinpoint where reasoning breaks down
• Benchmark and compare model outputs against defined technical taxonomies and performance metrics

Requirements

• BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative field with a machine learning focus
• Hands-on production experience building, deploying, or fine-tuning ML models
• Professional-level understanding of neural network architectures such as Transformers, CNNs, and RNNs, plus optimization techniques
• Practical experience with prompt engineering, RLHF, or retrieval-augmented generation (RAG) workflows
• Ability to audit model logic, flag potential training-data contamination, and evaluate the mathematical proofs behind ML algorithms
• Strong attention to detail for spotting hallucinations, biased outputs, or logical failures in AI-generated technical content

Nice to have

• Proficiency with PyTorch or TensorFlow/Keras
• Advanced Python skills including NumPy, Pandas, Scikit-learn, and Hugging Face Transformers
• Experience with cloud MLOps platforms such as AWS SageMaker, Google Cloud Vertex AI, Weights & Biases, or LangChain
• Familiarity with vector databases like Pinecone, Milvus, or Weaviate for RAG evaluation

Benefits and work setup

• Hourly compensation commonly up to $80, depending on study requirements
• Fully remote with flexible scheduling aligned to available studies
• Short 10–15 minute skills assessment as the entry point, with quick onboarding once accepted