AI Engineer/ML Engineer - Senior Developers - AI Training - San Francisco, US

prolificacademicltd; USA; Remote; USD 83.000 - 110.000; Vollzeit

Benefits dieser Stelle

Remote engagement

Flexible hours

Short skills assessment

Zusammenfassung

prolificacademic ltd seeks senior AI/ML engineers for remote, task-based research contributions. You will review model explanations, audit training loops, and provide structured feedback to advance RLHF and alignment. The role emphasizes deep technical evaluation and careful attention to bias and correctness.

Remote, flexible hours, candidates near San Francisco or willing to work US-time zones. Short entry assessment required; tasks typically fit within one hour sessions.

Qualifikationen

• Degree in a quantitative ML field with emphasis on ML research.
• Proven experience building, deploying, or fine-tuning ML models.
• Strong grasp of Transformers, CNNs, RNNs and optimization techniques.
• Experience with prompt engineering, RLHF or RAG workflows.
• Attention to detail for detecting hallucinations or data issues.

Aufgaben

• Evaluate explanations of model architectures, loss functions, and backpropagation.
• Audit ML code and notebooks, including training loops and evaluations.
• Provide structured feedback to support RLHF and model alignment.
• Assess how models reason on chain-of-thought prompts and identify failures.
• Run benchmarks comparing model outputs using technical taxonomies.

Kenntnisse

ML model evaluation

RLHF

Prompt engineering

Model alignment

Theory of ML proofs

Ausbildung

Bachelor's degree in CS/AI/Robotics

Master's or PhD in ML-focused field

Tools

PyTorch

TensorFlow/Keras

Weights & Biases

LangChain

Pinecone

Jobbeschreibung

Role overview

This participant-based role is open to senior AI and machine learning engineers interested in shaping the next generation of large language models through paid research contributions. Rather than a traditional hire, contributors complete flexible, remote tasks on their own schedule while bringing deep technical expertise to model evaluation and alignment work. It is designed for experienced practitioners based in or near San Francisco who can deliver focused, high-quality technical review.

Responsibilities

• Evaluate AI-generated explanations of model architectures, loss functions, and backpropagation for technical correctness.
• Audit machine learning code and notebooks, including training loops, preprocessing scripts, and model evaluations.
• Provide structured human feedback that powers reinforcement learning from human feedback (RLHF) and supports model alignment.
• Critically assess how AI models traverse complex chain-of-thought prompts and identify where reasoning fails.
• Run comparative benchmarks between model outputs using specified technical taxonomies and performance metrics.

Requirements

• Bachelor's, Master's, or doctoral degree in Computer Science, Artificial Intelligence, Robotics, or a closely related quantitative discipline with a machine learning focus.
• Proven experience building, deploying, or fine-tuning ML models in production environments.
• Strong grasp of neural network architectures such as Transformers, CNNs, and RNNs, paired with knowledge of optimization techniques.
• Practical experience with prompt engineering, RLHF, or retrieval-augmented generation (RAG) workflows.
• Keen attention to detail for detecting hallucinations, biased outputs, or logical flaws in AI-generated technical material.
• Ability to audit intricate model logic, assess mathematical proofs behind ML algorithms, and recognize training data contamination.

Nice to have

• Deep proficiency with PyTorch or TensorFlow/Keras.
• Advanced Python skills covering NumPy, Pandas, Scikit-learn, and Hugging Face Transformers.
• Experience with AWS SageMaker, Google Cloud Vertex AI, or tools like Weights & Biases and LangChain.
• Familiarity with vector databases such as Pinecone, Milvus, or Weaviate in RAG contexts.

Benefits and work setup

• Pay rates reaching up to $80 per hour for qualified participants.
• Fully remote engagement with flexible hours.
• Short 10 to 15 minute skills assessment as the entry point.
• Individual tasks usually fit within one uninterrupted hour, with many sessions shorter than that.