Forward Deployed Engineer for AI Systems

As a Forward Deployed Engineer, you will play a pivotal role in collaborating with leading AI labs and enterprises, serving as a technical research and implementation partner. This position is at the intersection of applied AI, machine learning infrastructure, data intelligence, and product development, where you will help shape research directions, curate high-quality data, and implement machine learning and evaluation pipelines.

• Work directly 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 machine learning 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.
• Collaborate 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 their lifecycle, including discovery, architecture, implementation, deployment, reliability, iteration, and partner success.

• Ability to operate independently in ambiguous, partner-facing settings with strong technical and product ownership.
• Proficient Python engineer with experience in building and shipping production systems end to end.
• Experience working 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.

• Background in 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, and model evaluation methodologies.

Full-time position with remote work options and travel requirements.

Annual salary ranging from $300, 000 to $650, 000.

Open to candidates with the required skills and experience, including those who can operate in a remote environment.