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

prolificacademicltd; USA; Remote; USD 91.000 - 129.000; Vollzeit

Benefits dieser Stelle

Up to $80/hour

Fully remote

Zusammenfassung

Prolific Academic Ltd in the United States is seeking senior AI/ML engineers to help train and evaluate cutting-edge large language models through paid research tasks. You will work remotely with flexible scheduling, applying professional judgment to model review, alignment, and benchmarking.

The role targets experienced Seattle-area engineers who can deliver focused, uninterrupted technical analysis, with tasks including reviewing model explanations, validating code, and providing high-quality

Qualifikationen

• BS, MS or PhD in CS/AI/related quantitative field with ML concentration.
• Professional experience building, deploying, or fine-tuning ML models.
• Working knowledge of Transformers, CNNs, RNNs, and optimization techniques.
• Hands-on familiarity with prompt engineering, RLHF, or retrieval-augmented generation workflows.
• Strong attention to detail in identifying hallucinations, biases, or failures in AI content.

Aufgaben

• Review explanations of model architectures, loss functions, and backpropagation for accuracy.
• Validate ML code and notebooks, including training loops and preprocessing scripts.
• Provide high-quality human feedback for RLHF for model alignment.
• Evaluate model responses to complex prompts and identify reasoning breakdowns.
• Benchmark model outputs across defined metrics and taxonomies.

Kenntnisse

Neural networks

Transformers

Prompt engineering

RLHF

RAG workflows

Ausbildung

MS/PhD in CS/AI

Tools

PyTorch

TensorFlow

Keras

NumPy

Pandas

Scikit-learn

Hugging Face

Weights & Biases

LangChain

SageMaker

Vertex AI

Pinecone

Milvus

Weaviate

Jobbeschreibung

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.