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

Prolific; Raleigh (NC); Hybrid; USD 100,000 - 130,000; Full time

Benefits offered by this job

Competitive pay rates

Flexible hours

Ability to work from home

Job summary

Prolific, located in Raleigh, North Carolina, is seeking an AI & Machine Learning Engineer specialized in training and evaluating large language models. The ideal candidate will have a strong background in deep learning and familiarization with contemporary frameworks.

Responsibilities include auditing model logic, validating ML code, and conducting performance benchmarks. Benefits feature competitive pay, flexible hours, and remote work options.

Qualifications

• Experience building, deploying, or fine-tuning ML models in a production environment.
• Professional-level understanding of neural network architectures and optimization techniques.
• Hands-on experience with Prompt Engineering, RLHF, or RAG workflows.

Responsibilities

• Review AI-generated explanations of model architectures for technical accuracy.
• Validate ML-specific code for efficiency and correctness.
• Provide high-quality human feedback to align models with human intent.

Skills

Deep Learning Mastery

LLM Specialization

Technical Rigor

Analytical Critique

Education

BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or related field

Tools

PyTorch

TensorFlow/Keras

AWS (SageMaker)

Google Cloud (Vertex AI)

Hugging Face Transformers

Job description

AI & Machine Learning Engineer – AI Training

This role focuses on training and evaluating large language models, leveraging deep machine learning expertise.

What You’ll Bring

• Education: a BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative field with a focus on Machine Learning.
• Professional Experience: experience building, deploying, or fine‑tuning ML models in a production environment.
• Deep Learning Mastery: professional-level understanding of neural network architectures (Transformers, CNNs, RNNs) and optimization techniques.
• LLM Specialization: hands‑on experience with Prompt Engineering, RLHF (Reinforcement Learning from Human Feedback), or RAG (Retrieval‑Augmented Generation) workflows.
• Technical Rigor: the ability to audit complex model logic, identify training data contamination, and evaluate mathematical proofs behind ML algorithms.
• Analytical Critique: high attention to detail in spotting hallucinations, biased outputs, or logical failures in AI-generated technical content.

What You’ll Be Doing in the Role

• Evaluate LLM Architecture Logic: review AI‑generated explanations of model architectures, loss functions, and backpropagation for technical accuracy.
• Audit Code & Notebooks: validate ML‑specific code (e.g., training loops, data preprocessing scripts, or model evaluations) for efficiency and correctness.
• Refine RLHF Frameworks: provide high‑quality human feedback necessary to align models with human intent, safety, and helpfulness.
• Analyze Model Reasoning: critically assess how an AI model navigates complex chain‑of‑thought prompts and identify where the reasoning breaks down.
• Benchmark Performance: conduct comparative testing between different model outputs based on specific technical taxonomies and performance metrics.

Key Technologies

• Frameworks: expert proficiency in PyTorch or TensorFlow/Keras.
• Language & Data: advanced Python (NumPy, Pandas, Scikit‑learn) and experience with Hugging Face Transformers.
• Cloud & MLOps: experience with AWS (SageMaker), Google Cloud (Vertex AI), or specialized tools like Weights & Biases and LangChain.
• Vector Databases: familiarity with Pinecone, Milvus, or Weaviate for RAG evaluation.

Benefits

Competitive pay rates, flexible hours, and the ability to work from home.

We provide a platform that connects researchers and companies with a global pool of participants, enabling the collection of high‑quality, ethically sourced human behavioural data and feedback.

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