Large Language Models (LLMs) Jobs

I’m building an **AI-powered Brazilian Tax Intelligence platform** designed to identify tax opportunities, risks, credits, and applicable tax strategies for companies. The system will analyze company and tax data and cross-reference it with a structured legal database containing **Brazilian tax legislation, administrative rulings, court decisions (CARF, STJ, STF), tax authority guidance, tax regimes, and state-specific benefits**. The platform should use AI to: * Analyze company and tax data; * Identify potential tax opportunities and risks; * Match companies with applicable tax rules and precedents; * Explain the reasoning behind each finding; * Provide the legal sources supporting the analysis; * Monitor changes in legislation and case law. The architecture must support different *...

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Senior ML Engineer / Advisor / Technical co-founder: Model Post-Training & Alignment About Bentham Research Grounding Machine Intelligence in the Humanities Bentham is an applied research lab working at the intersection of AI and the humanities. We build doctorate-authored, peer-reviewed evaluation instruments, training environments, and datasets for frontier AI labs, enterprises, and governments. Our core focus spans ethics, moral reasoning, philosophy, political theory, law, theology, and history. We believe expanding model capabilities requires anchoring machine judgment in human wisdom through bottom-up, scholar-led workflows paired with AI-in-the-loop validation. The Role We are seeking an ML Research Engineer or Technical Advisor with hands-on experience in post-training model...

Buscamos un desarrollador senior o un equipo pequeño para construir y lanzar el MVP de una Web App B2B enfocada en Supply Chain Planning. El objetivo no es construir desde el comienzo una plataforma empresarial terminada. Queremos poner en producción, en el menor tiempo razonable, una versión funcional para clientes reales, aprender con cada engagement y evolucionarla mediante ciclos continuos de mejora. Esperamos mantener una relación de largo plazo con el desarrollador seleccionado. Después del lanzamiento serán necesarios ajustes en preguntas, reglas, cálculos, experiencia, análisis de IA y reportes según la evidencia obtenida con clientes. El MVP debe funcionar de extremo a extremo, pero puede conservar intervenció...