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

prolificacademicltd; United States; Remote; USD 55,000 - 110,000; Part time

Job summary

prolificacademic ltd is seeking senior AI and ML engineers for a flexible, remote contractor role supporting the training and evaluation of large language models. You will review explanations, audit code, and provide high-quality human feedback used to align models with human intent.

Ideal candidates hold a BS/MS/PhD in a quantitative field, have hands-on production ML experience, and are proficient with modern architectures and tooling. This is a paid hourly engagement, not a full-time position.

Qualifications

• BS/MS/PhD in Computer Science, Artificial Intelligence, Robotics, or related quantitative field with focus on ML.
• Hands-on experience building, deploying, or fine-tuning ML models in production.
• Strong understanding of neural network architectures (Transformers, CNNs, RNNs) and optimization techniques.
• Experience with prompt engineering, RLHF, or retrieval-augmented generation workflows.
• Ability to audit complex model logic, identify data contamination, and evaluate ML proofs.

Responsibilities

• Review AI-generated explanations of model architectures, loss functions, and backpropagation for technical accuracy.
• Audit ML code and notebooks, including training loops and data preprocessing, for efficiency and correctness.
• Provide high-quality human feedback used in RLHF and alignment processes.
• Analyze how models navigate chain-of-thought prompts and identify reasoning failures.
• Conduct benchmarking between model outputs using technical taxonomies and metrics.

Skills

Transformer architectures

RLHF

Prompt engineering

Python

ML production experience

Education

PhD in Computer Science / AI

Tools

PyTorch

TensorFlow/Keras

Hugging Face Transformers

AWS SageMaker

Job description

Role overview

This is a flexible, remote, paid opportunity for senior AI and machine learning engineers to join an expert network that supports the training and evaluation of large language models. Participants contribute deep technical expertise through structured tasks such as reviewing model explanations, auditing ML code, and providing high-quality human feedback used to align models with human intent. It is a contractor-style engagement rather than a traditional full-time role, with paid hourly work that fits around other commitments.

Responsibilities

• Review AI-generated explanations of model architectures, loss functions, and backpropagation for technical accuracy.
• Audit machine learning code and notebooks, including training loops, data preprocessing scripts, and model evaluations, for efficiency and correctness.
• Provide high-quality human feedback used in RLHF and alignment processes to help models behave helpfully, safely, and as intended.
• Critically analyze how an AI model navigates complex chain-of-thought prompts and pinpoint where reasoning breaks down.
• Conduct comparative benchmarking between different model outputs using specific technical taxonomies and performance metrics.

Requirements

• 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 such as Transformers, CNNs, and RNNs, and their optimization techniques.
• Hands-on experience with prompt engineering, RLHF, or 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.

Nice to have

• Expert proficiency with PyTorch or TensorFlow/Keras.
• Advanced Python skills with NumX, Pandas, Scikit-learn, and experience with Hugging Face Transformers.
• Familiarity with AWS SageMaker, Google Cloud Vertex AI, Weights & Biases, or LangChain.
• Familiarity with vector databases such as Pinecone, Milvus, or Weaviate for RAG evaluation.

Benefits and work setup

• Fully remote, flexible engagement; participants can start after passing a short skills assessment.
• Competitive hourly pay, often up to around $80 per hour depending on the study.
• Tasks typically require up to one hour of uninterrupted work, with many shorter than that.