Forward Deployed Engineer

Responsibilities

• 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 machine learning 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 transform one-off AI experiments into reliable, repeatable, multi-turn agent workflows.
• Own technical systems across the full lifecycle, including discovery, architecture, implementation, deployment, reliability, iteration, and partner success.
• Work directly with technical partners, researchers, founders, and enterprise stakeholders throughout project execution.

Skills

Requirements

• Ability to operate independently in ambiguous and partner-facing environments with strong technical and product ownership.
• Strong Python engineering skills with experience building and shipping production systems end to end.
• Experience working with large language models, 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 understanding of data quality, taxonomy design, labeling workflows, and dataset curation for AI systems.
• Comfort 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 is preferred.
• Experience building systems for multi-turn agents, agent evaluation, workflow automation, or human-in-the-loop AI is preferred.
• Experience designing data taxonomies, annotation systems, evaluation rubrics, or dataset quality pipelines is preferred.
• Experience acting as a technical partner to external customers, research teams, or strategic enterprise accounts is preferred.
• Familiarity with modern LLM tooling, agent frameworks, model evaluation stacks, and ML experimentation platforms is preferred.
• Ability and willingness to travel as required by the role.

Benefits

• Full-time remote position with travel required.
• Base salary of $180,000–$250,000 per year.
• Eligibility for equity compensation.
• Potential performance-based bonuses subject to role and company policies.
• Up to 100% reimbursement for health insurance premiums.
• Paid time off.
• 401(k) plan with company match.
• Additional benefits supporting a high-performing remote-first workforce.
• Opportunity to work directly with leading AI labs and enterprise partners.
• Opportunity to build production-grade LLM, agentic, RAG, data intelligence, and ML infrastructure systems.