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

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

Benefits offered by this job

Fully remote

Flexible hours

Short onboarding

Job summary

Prolific Academic Ltd in the United States seeks senior AI/ML engineers based in Jacksonville for a paid participant role in LLM training and evaluation. Engagement is task-based, with compensation tied to hours contributed, not a traditional job.

Responsibilities include assessing explanations of neural architectures, validating ML code and notebooks, and supplying structured feedback for RLHF. Fully remote, flexible hours, with many tasks fitting within about one hour of work.

Qualifications

• BS/MS/PhD in CS/AI/Robotics or related quantitative field with ML focus.
• Experience building, deploying, or fine-tuning ML models in production.
• Knowledge of Transformers, CNNs, RNNs and optimization methods.
• Hands-on prompt engineering, RLHF or retrieval-augmented generation workflows.
• Ability to audit model logic and detect data contamination.

Responsibilities

• Assess AI explanations of architectures, loss functions, and backpropagation for technical accuracy.
• Validate ML code and notebooks, including training loops and preprocessing scripts.
• Provide structured human feedback powering RLHF pipelines.
• Trace how models handle chain-of-thought prompts and identify breakdowns.
• Run benchmarks comparing outputs against defined metrics.

Skills

ML model eval

Prompt engineering

RLHF

RAG workflows

Transformers

PyTorch

TensorFlow

Keras

Education

CS/AI/Robotics degree

Tools

PyTorch

TensorFlow

Keras

NumPy

Pandas

LangChain

Job description

Role overview

This is a paid participant opportunity for senior AI and machine learning engineers based in Jacksonville to take part in large language model training and evaluation work. Engagement happens through structured, per-study tasks rather than a traditional employment arrangement, with compensation tied to the hours contributed. The work centers on applying rigorous technical judgment to model outputs, code, and reasoning traces used to train the next generation of AI systems.

Responsibilities

• Assess AI-generated explanations of neural architectures, loss functions, and backpropagation for technical accuracy
• Validate ML-specific code and notebooks, including training loops, preprocessing scripts, and evaluation routines, flagging inefficiencies and bugs
• Supply structured human feedback that powers reinforcement learning from human feedback (RLHF) pipelines, helping align models with intent, safety, and helpfulness
• Critically trace how models handle chain-of-thought prompts and identify where reasoning breaks down
• Run comparative benchmarks of model outputs against defined taxonomies and performance metrics

Requirements

• BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative discipline with a machine learning focus
• Professional track record building, deploying, or fine-tuning ML models in production settings
• Working command of neural network architectures including Transformers, CNNs, and RNNs, along with their optimization techniques
• Hands‑on experience with prompt engineering, RLHF, or retrieval‑augmented generation (RAG) workflows
• Ability to audit complex model logic, detect training data contamination, and evaluate the mathematical foundations of ML algorithms
• Sharp attention to detail for spotting 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 platforms 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 10 to 15 minute skills assessment followed by fast onboarding
• Most tasks are designed to fit within about one hour of focused, uninterrupted work, with many shorter in length