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

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

Zusammenfassung

prolificacademicltd seeks seasoned AI/ML engineers to contribute to training and evaluation of large language models. This is a remote, flexible, task-based engagement rather than a traditional role.

Senior engineers with deep ML expertise can dedicate focused blocks of time for technical review work, ideally based in or near Sacramento. Responsibilities include auditing model explanations, ML code, RLHF feedback, and benchmarking model outputs.

Qualifikationen

• Bachelors/Masters/PhD in CS/AI/Robotics with ML focus.
• Hands-on experience deploying or fine-tuning ML models in production.
• Experience with neural nets: Transformers, CNNs, RNNs.
• Experience with prompt engineering and RLHF workflows.
• Attention to detail for hallucinations, biases, or logical failures.
• Ability to review ML proofs and assess data contamination.

Aufgaben

• Assess explanations of model architectures and loss functions for soundness.
• Audit ML code, notebooks, training loops, and evaluation pipelines.
• Provide structured human feedback for RLHF pipelines.
• Evaluate reasoning on chain-of-thought prompts and breakdowns.
• Run benchmarks across model outputs using defined metrics.

Kenntnisse

Machine learning

Transformers

RNNs

CNNs

Prompt engineering

RLHF workflows

Proofs review

Data contamination

Production ML

Ausbildung

Bachelor's degree

Master's degree

PhD

Tools

PyTorch

TensorFlow

NumPy

Pandas

Scikit-learn

Hugging Face

Weights & Biases

LangChain

SageMaker

Vertex AI

Pinecone

Milvus

Weaviate

Jobbeschreibung

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.