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

prolificacademicltd; United States; Remote; USD 83,000 - 110,000; Full time

Benefits offered by this job

Up to $80 per hour

Fully remote work

Flexible scheduling

Job summary

Prolific Academic Ltd. seeks senior AI/ML engineers to train and evaluate cutting-edge language models through paid research tasks. Work remotely with flexible scheduling, auditing model logic and data quality, and providing detailed human feedback for RLHF pipelines.

The role focuses on evaluating prompts, improving alignment, and benchmarking model outputs with expert judgment from experienced Seattle-area engineers. Remote setup with a short onboarding assessment.

Qualifications

• Degree in CS/AI/robotics or related quantitative field with ML concentration.
• Production ML experience: building, deploying or fine-tuning models.
• Knowledge of transformers, CNNs, RNNs and optimization techniques.
• Familiarity with RLHF, prompt engineering and RAG workflows.
• Detail-oriented in auditing model logic and data quality.

Responsibilities

• Review explanations of model architectures, loss functions, and backpropagation.
• Validate ML code and notebooks, including training loops and data prep.
• Provide high-quality human feedback for RLHF pipelines.
• Evaluate model responses and identify reasoning breakdowns in prompts.
• Benchmark outputs across defined metrics and taxonomies.

Skills

Attention to detail

Prompt engineering

Model evaluation

Analytical thinking

Communication

Education

BS/MS/PhD in Computer Science or related field

Tools

PyTorch

TensorFlow/Keras

NumPy

Pandas

Scikit-learn

Hugging Face Transformers

AWS SageMaker

LangChain

Pinecone/Milvus/Weaviate

Job description

Role overview

This participant-focused opportunity is designed for senior AI and machine learning engineers who want to help train and evaluate cutting-edge large language models through paid research tasks. Contributors work remotely with flexible scheduling, applying professional judgment to model review, alignment, and benchmarking. The arrangement fits experienced Seattle-area engineers who can deliver focused, uninterrupted technical analysis.

Responsibilities

• Review AI-generated explanations of model architectures, loss functions, and backpropagation for technical accuracy.
• Validate machine learning code and notebooks, including training loops, data preprocessing scripts, and evaluation logic.
• Deliver high-quality human feedback used in reinforcement learning from human feedback (RLHF) pipelines for model alignment.
• Examine how models respond to complex chain-of-thought prompts and locate specific reasoning breakdowns.
• Perform comparative benchmarking across model outputs according to defined taxonomies and performance metrics.

Requirements

• BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative field with a machine learning concentration.
• Professional experience building, deploying, or fine-tuning ML models in production settings.
• Working command of neural network architectures including Transformers, CNNs, and RNNs, and associated optimization techniques.
• Hands-on familiarity with prompt engineering, RLHF, or retrieval-augmented generation (RAG) workflows.
• Strong attention to detail when identifying hallucinations, biased outputs, or logical failures in AI-generated technical content.
• Ability to audit complex model logic, evaluate mathematical proofs behind ML algorithms, and detect training data contamination.

Nice to have

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

Benefits and work setup

• Compensation up to $80 per hour for qualifying contributors.
• Fully remote work with flexible scheduling and the ability to work from home.
• Onboarding through a brief 10 to 15 minute skills assessment.
• Most tasks require one hour of uninterrupted focus, with many sessions shorter in duration.