AI Engineer/ML Engineer - Senior Developers - AI Training - Dallas, USA

prolificacademicltd; United States; Remote; USD 66,000 - 110,000; Full time

Benefits offered by this job

Fully remote

Flexible hours

Job summary

Prolific Academic Ltd is recruiting senior AI/ML engineers for a paid expert network. The work is remote and project-based, focusing on rigorous technical review, code auditing, and structured feedback to advance LLMs.

Responsibilities include evaluating architectures, auditing training loops, contributing RLHF-aligned feedback, and inspecting model behavior for bias and hallucinations. Strong Python and ML framework skills are expected.

Qualifications

• Degree in CS/AI/Robotics or related quantitative field with ML emphasis.
• Experience building or deploying ML models in production.
• Strong grasp of neural architectures (Transformers, CNNs, RNNs) and optimization methods.
• Experience with prompt engineering, RLHF, or retrieval-augmented generation is desirable.
• Ability to inspect model behavior and identify data contamination or algorithmic issues.

Responsibilities

• Evaluate AI-generated technical content on model architectures and loss functions.
• Audit ML code, training loops, and data preprocessing scripts for efficiency and reliability.
• Provide high-quality human feedback to support RLHF and model alignment.
• Examine chain-of-thought reasoning and identify when AI logic fails.
• Benchmark outputs against defined taxonomies and performance criteria.

Skills

ML deployment

Transformers

Python programming

Model evaluation

Education

Bachelor's/Master's/PhD in CS or related

Tools

PyTorch

TensorFlow

Hugging Face

Weights & Biases

LangChain

Job description

Role overview

A paid expert network is recruiting senior AI and machine learning engineers to help train and assess the next generation of large language models. The work is remote and project-based, centering on rigorous technical review, code auditing, and structured feedback rather than ongoing product engineering.

Responsibilities

• Evaluate AI-generated technical content on model architectures, loss functions, and backpropagation for correctness
• Audit ML code, training loops, and data preprocessing scripts to confirm efficiency and reliability
• Contribute high-quality human feedback that supports reinforcement learning from human feedback and model alignment efforts
• Examine chain-of-thought reasoning and identify where an AI model's logic fails or diverges
• Benchmark model outputs against defined taxonomies and performance criteria

Requirements

• Degree (BS, MS, or PhD) in Computer Science, Artificial Intelligence, Robotics, or a quantitative discipline with an ML emphasis
• Demonstrated experience building, deploying, or fine-tuning ML models in production
• Strong grasp of neural network architectures including Transformers, CNNs, and RNNs, along with optimization methods
• Practical experience with prompt engineering, RLHF, or retrieval-augmented generation systems
• Capacity to inspect complex model behavior, detect training data contamination, and reason about algorithmic proofs
• Meticulous attention to detail in spotting hallucinations, bias, and logical flaws

Nice to have

• Deep proficiency in PyTorch or TensorFlow/Keras
• Strong Python skills using NumPy, Pandas, Scikit-learn, and the Hugging Face Transformers library
• Familiarity with cloud ML platforms like AWS SageMaker or Google Vertex AI, plus tooling such as Weights & Biases and LangChain
• Exposure to vector databases such as Pinecone, Milvus, or Weaviate

Benefits and work setup

• Fully remote, task-based engagement with flexible hours
• Hourly compensation reaching up to around $80 for in-demand tasks; many studies run less than an hour