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

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

Benefits offered by this job

Flexible scheduling

Fully remote

Job summary

Prolific Academic Ltd invites experts to join a participant-based, paid contract role focused on machine learning. You will contribute to the training and evaluation of large language models, with a role based in Boston and a fully remote, flexible schedule.

You will review explanations, audit ML code, provide structured feedback for RLHF workflows, and assess reasoning in chain-of-thought prompts. Expect up to one hour per study, with tasks aligned to available studies.

Qualifications

• BS/MS/PhD in CS/AI/Robotics or related field with ML focus.
• Hands-on production experience building, deploying, or fine-tuning ML models.
• Strong understanding of neural networks including Transformers, CNNs, and RNNs; knowledge of optimization techniques.
• Experience with prompt engineering, RLHF, or retrieval-augmented generation (RAG).
• Ability to audit model logic, flag training-data issues, and evaluate ML proofs.

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 supporting RLHF workflows and model alignment.
• Assess how models handle chain-of-thought prompts and identify reasoning breakdowns.
• Benchmark model outputs against defined technical taxonomies and performance metrics.

Skills

ML model deployment

Transformer architectures

Prompt engineering

RLHF workflows

RAG workflows

PyTorch

TensorFlow

Python programming

Education

BS/MS/PhD in ML related field

Tools

Hugging Face Transformers

AWS SageMaker

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 based in Boston 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