Machine Learning & NLP Expert

Key Responsibilities

• Design challenging, real-world machine learning and natural language processing tasks covering areas such as: Machine Learning Model Development and Evaluation Natural Language Understanding (NLU) Natural Language Generation (NLG) Information Retrieval and Search Applied Machine Learning Pipelines Transformer Models and Large Language Models (LLMs)
• Develop accurate reference solutions and integrate tasks into agentic development environments using Python.
• Build executable evaluation frameworks and testing components where appropriate.
• Evaluate AI model outputs for technical correctness, reasoning quality, and overall performance.
• Create and refine evaluation guidelines, scoring rubrics, and quality standards for ML and NLP tasks.
• Collaborate with fellow subject matter experts to ensure consistency, accuracy, and high-quality training data.

Required Qualifications

• Deep hands-on experience in Machine Learning and/or Natural Language Processing through industry, research, or graduate/PhD-level work.
• Strong proficiency in Python with practical experience developing ML or NLP applications.
• Strong understanding of modern machine learning techniques, including: Model Training and Evaluation Transformer Architectures Large Language Models (LLMs) NLP Pipelines Feature Engineering and Model Optimization
• Experience with industry-standard frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, or equivalent.
• Ability to commit approximately 20 hours per week.
• Excellent written communication skills and the ability to work independently in a remote environment.

Preferred Qualifications

• Experience in AI model evaluation, AI training data creation, or human-in-the-loop model assessment.
• Familiarity with Retrieval-Augmented Generation (RAG), vector databases, embedding models, or multimodal AI systems.
• Experience building benchmarking frameworks, automated evaluation pipelines, or testing infrastructure.
• Contributions to open-source ML/NLP projects or published research are a plus.
• Experience working with production-scale machine learning systems.