Forward Deployed Engineer for AI Systems

Partner with leading AI labs and enterprise teams as a technical research and implementation lead to design, build, and deploy data and model systems that turn expert knowledge into production-ready AI workflows. This role combines applied AI research, ML infrastructure work, data intelligence, and partner-facing product execution to deliver multi-turn agentic systems, RAG pipelines, and high-quality training and evaluation data.

• Collaborate directly with strategic AI labs and enterprise partners to define research goals, technical requirements, and project direction.
• Design and build large-scale data intelligence systems for collecting, organizing, evaluating, and improving training and evaluation datasets.
• Implement ML pipelines for data curation, model training, evaluation, experimentation, and continuous improvement.
• Create data taxonomies, labeling systems, and quality frameworks that improve dataset structure and model performance.
• Develop LLM applications, including multi-agent systems, tool-using agents, retrieval-augmented generation workflows, evaluation harnesses, and human-in-the-loop systems.
• Translate ambiguous AI research questions into scoped technical projects and production systems in partnership with research and engineering teams.
• Build infrastructure for model inference, experimentation, evaluation, and deployment across frontier AI platforms.
• Engineer systems that move partners from one-off experiments to reliable, repeatable multi-turn agent workflows.
• Own systems across the full lifecycle, including discovery, architecture, implementation, deployment, reliability, iteration, and partner success.

• Strong Python engineering skills with experience building and shipping production systems end to end.
• Experience working with LLMs, agentic systems, multi-turn workflows, tool use, retrieval-augmented generation, or AI automation.
• Experience building or maintaining data pipelines, ML infrastructure, evaluation systems, or research workflows.
• Solid understanding of data quality, taxonomy design, labeling workflows, and dataset curation for AI systems.
• Comfortable operating independently in ambiguous, partner-facing settings, with strong technical and product ownership.
• Experience working directly with technical partners, researchers, founders, or 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 and agent frameworks.

• Employment type: Full-time.
• Location: Remote, travel required as part of the role.
• Team: Core technical staff working directly with partners and internal research and engineering teams.

• Salary range: 300000 to 650000 yearly.

• Candidates must be able to work remotely and travel as needed for partner engagements. No visa, sponsorship, or citizenship details were specified in the source posting.