Forward Deployed Engineer for AI Systems

This role involves working directly with leading AI labs and enterprises as a technical research and implementation partner. As a Forward Deployed Engineer, you will operate at the intersection of applied AI, ML infrastructure, data intelligence, and partner-facing product development. Your contributions will help strategic partners define research directions, structure and curate high-quality data, implement ML and evaluation pipelines, and build agentic systems that enhance multi-turn agents and workflows in production. Key Responsibilities Collaborate with AI labs and enterprise partners to define research goals, technical requirements, and project direction. Build large-scale data intelligence systems for collecting, organizing, evaluating, and improving training and evaluation data. Implement ML pipelines for data curation, model training, evaluation, experimentation, and continuous improvement. Design data taxonomies, labeling systems, and quality frameworks to enhance dataset structure, model performance, and research outcomes. Develop LLM applications, including multi-agent systems, tool-using agents, RAG workflows, evaluation harnesses, and human-in-the-loop systems. Partner with research and engineering teams to translate ambiguous AI problems into scoped technical projects and production systems. Develop infrastructure for model inference, experimentation, evaluation, and deployment across frontier AI platforms. Build systems that transition partners from one-off AI experiments to reliable, repeatable, multi-turn agent workflows. Own systems throughout the full lifecycle, including discovery, architecture, implementation, deployment, reliability, iteration, and partner success. Qualifications Ability to operate independently in ambiguous, partner-facing settings with strong technical and product ownership. Strong Python engineering skills with experience in building and shipping production systems end to end. Experience with LLMs, agentic systems, multi-turn workflows, tool use, RAG, or AI automation. Experience in building or maintaining data pipelines, ML infrastructure, evaluation systems, or research workflows. Strong understanding of data quality, taxonomy design, labeling workflows, and dataset curation for AI systems. Comfortable working directly with technical partners, researchers, founders, and enterprise stakeholders. Preferred Qualifications Background in a startup, AI infrastructure company, applied AI company, or research-focused engineering team. Experience in building systems for multi-turn agents, agent evaluation, workflow automation, or human-in-the-loop AI. Experience designing data taxonomies, annotation systems, evaluation rubrics, or dataset quality pipelines. Experience acting as a technical partner to external customers, research teams, or strategic enterprise accounts. Familiarity with modern LLM tooling and agent development. Work Terms Full-time position with remote work and travel requirements. Compensation Annual salary ranging from $250, 000 to $400, 000. Eligibility Open to candidates with relevant experience and skills as outlined in the qualifications.