Forward Deployed Engineer

• Work directly with leading 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 that improve 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 help partners move from one-off AI experiments to reliable, repeatable, multi-turn agent workflows.
• Own systems across the full lifecycle, including discovery, architecture, implementation, deployment, reliability, iteration, and partner success.

• Able to operate independently in ambiguous, partner-facing settings with strong technical and product ownership.
• Strong Python engineer with experience building and shipping production systems end to end.
• Experience working with LLMs, agentic systems, multi-turn workflows, tool use, RAG, or AI automation.
• Built or maintained 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.

• Background at a startup, AI infrastructure company, applied AI company, or research-focused engineering team.
• Experience 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, agent frameworks, model evaluation stacks, and ML experimentation platforms.

RequiredAlso useful

Project and contracting process: micro1. Applications continue on the provider's website.