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

prolificacademicltd; United States; Remote; USD 66,000 - 110,000; Full time

Benefits offered by this job

Fully remote engagement

Flexible scheduling

Skills assessment before paid tasks

Job summary

prolificacademicltd is seeking seasoned AI/ML engineers to contribute to training and evaluation tasks for large language models. This remote, flexible role targets senior engineers who can dedicate focused blocks of time to technical review work from the United States.

You will audit model explanations, review code, and provide structured feedback for RLHF pipelines to improve safety and usefulness. Onboarding includes a short skills assessment before paid tasks.

Qualifications

• Bachelor’s, Master’s or PhD in Computer Science, Artificial Intelligence, Robotics, or related quantitative discipline with a ML focus.
• Hands-on experience building, deploying, or fine-tuning ML models in production.
• Working knowledge of Transformers, CNNs, RNNs and optimization techniques.
• Practical exposure to prompt engineering, RLHF workflows, or retrieval-augmented generation (RAG).
• Strong attention to detail for spotting hallucinations or biased outputs in AI content.
• Ability to review mathematical proofs behind ML algorithms and identify data contamination.

Responsibilities

• Assess AI-generated explanations of model architectures, loss functions, and backpropagation for technical soundness.
• Audit ML code and notebooks, including training loops, preprocessing scripts, and evaluation pipelines.
• Provide structured human feedback used in RLHF pipelines to align models with user intent and safety.
• Examine how models handle complex chain-of-thought prompts and identify where reasoning breaks down.
• Run comparative benchmarks across model outputs using defined metrics.

Skills

ML engineering

Prompt engineering

RLHF workflows

Model evaluation

Education

Bachelor/Master/PhD in CS/AI/Robotics

Tools

PyTorch

TensorFlow/Keras

Job description

Role overview

This is a participant-based opportunity for seasoned AI and machine learning engineers to contribute to the training and evaluation of large language models. Rather than a traditional full-time position, contributors complete paid research tasks on a flexible, remote schedule, applying deep technical expertise to improve model quality. The arrangement suits senior engineers who can dedicate focused, uninterrupted blocks of time to technical review work and are based in or near Sacramento.

Responsibilities

• Assess AI-generated explanations of model architectures, loss functions, and backpropagation for technical soundness.
• Audit machine learning code and notebooks, including training loops, preprocessing scripts, and evaluation pipelines.
• Provide structured human feedback used in reinforcement learning from human feedback (RLHF) pipelines to support model alignment with user intent, safety, and helpfulness.
• Examine how models handle complex chain-of-thought prompts and pinpoint where their reasoning breaks down.
• Run comparative benchmarks across competing model outputs using defined technical taxonomies and performance metrics.

Requirements

• Bachelor's, Master's, or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative discipline with a machine learning focus.
• Hands-on experience building, deploying, or fine-tuning ML models in production settings.
• Working command of neural network architectures such as Transformers, CNNs, and RNNs, along with relevant optimization techniques.
• Practical exposure to prompt engineering, RLHF workflows, or retrieval-augmented generation (RAG).
• Strong attention to detail for spotting hallucinations, biased outputs, or logical failures in AI-generated technical content.
• Ability to review mathematical proofs behind ML algorithms and identify potential training data contamination.

Nice to have

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

Benefits and work setup

• Compensation reaching up to $80 per hour for qualified contributors.
• Fully remote engagement with flexible scheduling and the ability to work from home.
• Onboarding begins with a short 10 to 15 minute skills assessment before paid tasks are offered.
• Individual tasks generally require one uninterrupted hour, with many sessions shorter than that.