AI Engineer/ML Engineer - Senior Developers - AI Training - Germany

prolificacademicltd; United States; Remote; USD 91,000 - 129,000; Part time

Job summary

Prolific Academic Ltd. seeks senior AI/ML engineers to join an expert network contributing to large language model training and evaluation. Engagement is task-based, with participants paid per study for applying deep technical judgment to model outputs and ML artifacts.

The work shapes how next-generation AI systems reason, code, and align with human intent. Roles are fully remote with flexible hours and quick onboarding.

Qualifications

• BS, MS, or PhD in CS/AI/Robotics or related quantitative field with ML focus.
• Professional experience building, deploying, or fine-tuning ML models in production.
• Working knowledge of neural networks including Transformers, CNNs, and RNNs with optimization techniques.
• Hands-on with prompt engineering, RLHF, or retrieval-augmented generation (RAG).
• Ability to audit model logic, surface data contamination, and evaluate proofs behind ML algorithms.
• Attention to detail to catch hallucinations, biased outputs, and logical failures.

Responsibilities

• Assess explanations of neural architectures for technical correctness.
• Validate training loops, preprocessing scripts, and evaluation notebooks for efficiency and correctness.
• Provide structured feedback powering RLHF pipelines.
• Trace how models handle chain-of-thought prompts and identify breakdowns.
• Benchmark model outputs against defined technical taxonomies and metrics.

Skills

Transformers

CNNs

RNNs

Prompt engineering

RLHF

RAG

PyTorch

TensorFlow

Keras

Education

BS/MS/PhD in CS/AI

PhD in ML

Tools

PyTorch

TensorFlow

Keras

Hugging Face

Weights & Biases

LangChain

Pinecone

Job description

Role overview

This opportunity invites senior AI and machine learning engineers based in Germany to join an expert network that contributes to large language model training and evaluation. Engagement is task-based rather than salaried, with participants paid per study for applying deep technical judgment to model outputs and ML artifacts. The work directly influences how next-generation AI systems reason, code, and align with human intent.

Responsibilities

• Assess AI-generated explanations of neural architectures, loss functions, and backpropagation for technical correctness
• Validate ML code such as training loops, preprocessing scripts, and evaluation notebooks for efficiency and correctness
• Provide structured human feedback that powers reinforcement learning from human feedback (RLHF) pipelines, helping align models with safety and helpfulness criteria
• Critically trace how models handle chain-of-thought prompts and identify where reasoning breaks down
• Conduct comparative benchmarking of model outputs against defined technical taxonomies and performance metrics

Requirements

• BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or a closely related quantitative discipline with a machine learning focus
• Professional experience building, deploying, or fine-tuning ML models in production environments
• Working command of neural network architectures including Transformers, CNNs, and RNNs, plus their optimization techniques
• Hands-on experience with prompt engineering, RLHF, or retrieval-augmented generation (RAG) workflows
• Ability to audit complex model logic, surface training-data contamination, and evaluate the mathematical proofs behind ML algorithms
• Attention to detail strong enough to catch hallucinations, biased outputs, and logical failures in AI-generated technical content

Nice to have

• Expert proficiency in PyTorch or TensorFlow and Keras
• Advanced Python with NumPy, Pandas, and Scikit-learn, plus familiarity with Hugging Face Transformers
• Experience with cloud ML services such as AWS SageMaker or Google Vertex AI, and tooling like Weights and Biases or LangChain
• Familiarity with vector databases such as Pinecone, Milvus, or Weaviate for RAG evaluation

Benefits and work setup

• Hourly compensation that can reach up to $80 depending on the study
• Fully remote work with flexible hours
• Brief skills assessment followed by fast onboarding (around 15 minutes)
• Tasks generally fit within one hour of focused, uninterrupted work, with many shorter