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

prolificacademicltd; USA; Remote; USD 83.000 - 110.000; Vollzeit

Benefits dieser Stelle

Fully remote

Flexible engagement

Competitive hourly pay

Zusammenfassung

prolificacademicltd in the United States seeks senior AI/ML engineers for a flexible, remote contractor-style role that supports the training and evaluation of large language models.

You will review explanations, audit ML code and notebooks, provide high-quality human feedback for RLHF, and benchmark model outputs using technical taxonomies and metrics. The engagement is fully remote, hourly, and starts after a short skills assessment.

Qualifikationen

• Advanced degree in CS/AI or related quantitative field.
• Experience building, deploying, or fine-tuning ML models in production.
• Hands-on with neural networks such as Transformers, CNNs, RNNs and their optimization.

Aufgaben

• Review AI-generated explanations of model architectures, loss functions, and backpropagation for technical accuracy.
• Audit ML code and notebooks for efficiency and correctness.
• Provide high-quality human feedback for RLHF and alignment to guide model behavior.
• Analyze how an AI model handles chain-of-thought prompts and identify reasoning failures.
• Benchmark model outputs using defined taxonomies and metrics.

Kenntnisse

Transformers

Prompt engineering

RLHF

Code auditing

ML deployment

Ausbildung

Bachelor's/Master/PhD in CS/AI

Quantitative field focus

Tools

PyTorch

TensorFlow

Hugging Face

AWS SageMaker

Jobbeschreibung

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 NumPy, 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.