Expertise: coding_expert

Full-time role at micro1: work with AI labs and enterprises as a technical research and implementation partner: curate training and evaluation data, build ML and evaluation pipelines and agentic systems. Travel is required.

Full-time role at micro1: a hands-on AI engineering role designing and building agents and LLM-based automation for end-to-end expert operations, working with Talent Operations and Ops Engineering.

Full-time remote role at micro1: own end-to-end delivery of coding data pipelines used to train frontier AI models, drive automation of task generation, validation, QC and reporting, coordinate experts, reviewers and AI lab clients, and define task specifications, golden samples and review rubrics.

Remote part-time contract work (about 10–20 hours per week) for a research project with a leading AI lab: on your own Mac, carry out and narrate real software development workflows so the work can be used to benchmark and improve AI models. The work is done on your own computer: read the privacy terms on the official page before applying.

Remote part-time contract work (about 10–20 hours per week) for a research project with a leading AI lab: on your own Mac, carry out and narrate real data analysis workflows so the work can be used to benchmark and improve AI models. The work is done on your own computer: read the privacy terms on the official page before applying.

Remote part-time contract work (about 10–20 hours per week) for a research project with a leading AI lab: on your own Mac, carry out and narrate real productivity and automation workflows so the work can be used to benchmark and improve AI models. The work is done on your own computer: read the privacy terms on the official page before applying.

We are seeking coding experts with strong language skills to support AI training and evaluation projects. You will apply your programming expertise to review code, solve technical problems, and improve the accuracy and clarity of AI-generated responses in your language of expertise. Main tasks listed: Write, review, debug, and optimize code for various programming tasks; Evaluate AI-generated code for correctness, efficiency, readability, and adherence to instructions; Create and review coding prompts, solutions, and technical explanations in your language of expertise; Ensure technical responses are clear, linguistically accurate, and use appropriate programming terminology.

We are seeking highly qualified Domain Experts (SMEs) to support the evaluation, testing, and improvement of advanced Large Language Models (LLMs). You will apply your subject-matter expertise to assess AI-generated responses, identify knowledge gaps, design challenging prompts, and improve model accuracy, reliability, and reasoning. Main tasks listed: Create complex, domain-specific prompts to test LLM capabilities; Evaluate AI-generated responses for factual accuracy, reasoning, relevance, and overall quality; Identify factual errors, logical inconsistencies, knowledge gaps, and model weaknesses; Develop challenging test cases and edge cases that expose model limitations.

We are hiring US-based academic dermatologists to contribute senior clinical expertise to data curation for frontier AI models and labs. The work supports a longitudinal dermatology dataset used by AI developers, regulatory teams, and academic groups. This is the senior reviewer track of the project. You will lead clinical review on complex cases, contribute to the development of clinical evaluation frameworks, and serve as expert advisor for client teams reviewing AI model outputs. Main tasks listed: Lead senior-level clinical review on complex or ambiguous cases; Facilitate structured resolution of clinical disagreement among reviewers; Contribute to the development of clinical evaluation frameworks; Provide expert advisory hours to client teams reviewing AI model outputs. The listing says the work can involve sensitive or graphic content.

This role is part of a project supporting leading LLM companies. The primary objective is to help these foundational LLM companies improve their Large Language Models. We support companies in enhancing their models by offering high-quality proprietary data. This data can be used as a basis for fine-tuning models or as an evaluation set to benchmark the performance. Main tasks listed: Translate CUDA/C++ code into equivalent Python implementations using PyTorch and NumPy, ensuring logical and performance parity; Analyze CUDA kernels and GPU-accelerated code for structure, efficiency, and function before translation; Evaluate LLM-generated translations of CUDA/C++ code to Python, providing technical feedback and corrections; Collaborate with prompt engineers and researchers to develop test prompts that reflect real-world CUDA/PyTorch tasks.