• 2–4 years of hands-on experience in software/data/ML engineering in production environments • Very strong Python - clean, production-quality code (not just notebooks); sharp problem-solving and the aptitude to pick up MLOps practices quickly • Experience building and deploying APIs/services (FastAPI, Flask, or similar) and working knowledge of AWS (EC2, S3, Lambda) for hosting and serving • Understanding of the end-to-end ML lifecycle - training vs inference pipelines, deployment, and monitoring • Working knowledge of SQL and familiarity with PySpark; exposure to Databricks or comparable platforms, with the ability to read, refactor, and convert code for other environments • CI/CD and version-control fundamentals (Git, testing, rollback); familiarity with Docker; strong ownership and comfort with a broad, evolving scope and global stakeholders • Prior hands-on MLOps tooling experience (MLflow, model registries, drift detection, ML observability) • Experience supporting GenAI or LLM workloads operationally (model serving, inference pipelines, cost/performance tuning)

• Join a cutting-edge AI research initiative and help shape the next generation of frontier AI models. • Contribute technical expertise toward training and evaluating advanced AI systems. • Design challenging, real-world machine learning and natural language processing tasks. • Create high-quality reference solutions and assess AI model performance to identify reasoning gaps and improve overall capabilities. • 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. • Identify capability gaps, classify model failure modes, and provide detailed written analyses. • 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.

Role Description Join a cutting-edge AI research initiative and help shape the next generation of frontier AI models. We are seeking experienced Machine Learning & NLP Experts to contribute their technical expertise toward training and evaluating advanced AI systems. This is a part-time, fully remote opportunity requiring approximately 20 hours per week. - 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. - Identify capability gaps, classify model failure modes, and provide detailed written analyses. - 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. 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. Role Details - Employment Type: Independent Contractor - Work Arrangement: Fully Remote - Schedule: Approximately 20 hours per week - Project Duration: Based on project requirements and performance, with opportunities for extension Equal Opportunity We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request. Contract & Payment Terms - You will be engaged as an independent contractor. - This is a fully remote role that can be completed on your own schedule. - Projects may be extended, shortened, or concluded early depending on business needs and performance. - Your work will not involve access to confidential or proprietary information from any employer, client, or institution. - Payments are made weekly via Stripe or Wise based on services rendered. - Please note: We are unable to support H1-B or STEM OPT candidates at this time.

• Partner with research and engineering teams to strengthen AI model capabilities in MLOps, ML infrastructure, and large-scale training systems. • Design challenging, real-world MLOps and machine learning systems tasks that reflect production engineering scenarios. • Develop accurate, well-documented solutions to complex ML infrastructure and training pipeline problems. • Review and evaluate technical tasks and AI-generated solutions, providing clear and actionable written feedback. • Create detailed evaluation rubrics and scoring frameworks for topics including: - Distributed training architectures - ML pipeline design - Infrastructure optimization - Kernel-level programming - Performance tuning • Collaborate with fellow subject matter experts to maintain consistency, quality, and technical accuracy across training datasets. • Contribute domain expertise to improve the reasoning capabilities of advanced AI systems.