AI Engineer/ML Engineer - Senior Developers - AI Training - Long Beach, US

Prolific; Long Beach (CA); Hybrid; USD 100,000 - 150,000; Full time

Benefits offered by this job

Competitive pay rates

Flexible hours

Ability to work from home

Job summary

Prolific is looking for an AI & Machine Learning Engineer in Long Beach, California, to focus on training and evaluating large language models. This role demands deep machine learning expertise, with responsibilities that include auditing model logic, validating ML code, and critically assessing AI model reasoning.

The ideal candidate will hold a BS, MS, or PhD in a relevant field and be proficient with PyTorch and TensorFlow, as well as advanced knowledge of Python. This position offers competitive pay rates, flexible hours, and the option to work from home.

Qualifications

• Professional experience building, deploying, or fine-tuning ML models.
• Hands-on experience with RLHF workflows.
• Ability to audit complex model logic and identify training data contamination.

Responsibilities

• Review AI-generated explanations for technical accuracy.
• Validate ML-specific code for efficiency.
• Provide high-quality human feedback to align models.

Skills

Deep Learning Mastery

Prompt Engineering

Model Logic Auditing

Analytical Critique

Education

BS, MS, or PhD in Computer Science, AI, or related field

Tools

PyTorch

TensorFlow/Keras

Python (NumPy, Pandas, Scikit-learn)

AWS (SageMaker)

Google Cloud (Vertex AI)

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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