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

prolificacademicltd; United States; Remote; USD 91,000 - 129,000; Part time

Job summary

prolificacademicltd is offering a paid contract role in Austin with fully remote scheduling. You will review explanations of model architectures, audit training code, and provide feedback to RLHF workflows to improve model alignment and safety.

The position requires hands-on ML development experience and deep knowledge of Transformers, CNNs, and RNNs, with emphasis on debugging, data integrity, and bias spotting in AI outputs.

Qualifications

• BS, MS, or PhD in CS/AI/Robotics or related quantitative field with ML focus.
• Hands-on production experience building, deploying, or fine-tuning ML models.
• Knowledge of Transformer, CNNs, and RNN architectures and optimization.
• Experience with prompt engineering, RLHF, or retrieval-augmented generation (RAG).
• Ability to audit model logic and assess data contamination and proofs behind ML algorithms.
• Strong attention to detail to spot hallucinations or biased outputs in AI content.

Responsibilities

• Review AI-generated explanations of model architectures, loss functions, and backpropagation for technical correctness.
• Audit ML code, training loops, preprocessing scripts, and evaluation notebooks for efficiency and accuracy.
• Provide structured human feedback that supports 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 development

Neural networks

Prompt engineering

RLHF

Model evaluation

Code auditing

Education

BS/MS/PhD in CS/AI/Robotics or related quantitative field

Tools

PyTorch

TensorFlow/Keras

NumPy/Pandas

Scikit-learn

Hugging Face Transformers

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