About this opportunity
Confidential lists this Forward Deployed Engineer opportunity in encanto, California. Review the employer’s description below for duties, qualifications and application requirements.
Job description
Build production AI systems alongside leading AI labs and enterprise partners, turning open-ended research questions into dependable multi-turn agent workflows. This role combines applied AI research, ML infrastructure, data intelligence, and partner-facing engineering.
Key Responsibilities Work directly with AI lab and enterprise partners to define research goals, technical requirements, and project direction.
Develop data intelligence systems to collect, organize, evaluate, and improve training and evaluation data at scale.
Implement ML pipelines for data curation, model training, evaluation, experimentation, and continuous improvement.
Create data taxonomies, labeling systems, and quality frameworks that strengthen dataset structure, model performance, and research outcomes.
Build LLM applications, including multi-agent systems, tool-using agents, RAG workflows, evaluation harnesses, and human-in-the-loop systems.
Translate ambiguous AI challenges with research and engineering teams into scoped technical projects and production systems.
Develop infrastructure for model inference, experimentation, evaluation, and deployment across frontier AI platforms.
Help partners evolve one-off AI experiments into reliable, repeatable, multi-turn agent workflows.
Own systems across discovery, architecture, implementation, deployment, reliability, iteration, and partner success.
Qualifications Ability to work independently in ambiguous, partner-facing environments with strong technical and product ownership.
Strong Python engineering experience, including building and shipping end-to-end production systems.
Experience with LLMs, agentic systems, multi-turn workflows, tool use, RAG, or AI automation.
Experience building or maintaining data pipelines, ML infrastructure, evaluation systems, or research workflows.
Strong knowledge of data quality, taxonomy design, labeling workflows, and dataset curation for AI systems.
Comfort working directly with technical partners, researchers, founders, and enterprise stakeholders.
Preferred Qualifications Experience 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 serving as a technical partner to external customers, research teams, or strategic enterprise accounts.
Familiarity with modern LLM tooling, agent frameworks, and model evaluation stacks.
Work Terms Full-time role.
Remote work with required travel.
Compensation Annual compensation of $300, 000 to $650, 000.
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.