Forward Deployed Engineer | Full-time - Remote / Travel-required Remote (United States)

S27a; Northern (KY); Hybrid; USD 140,000 - 190,000; Full time

Job summary

S27a is seeking a Forward Deployed Engineer to collaborate directly with top AI labs and enterprise partners. You will shape research directions, build data pipelines, and deploy scalable ML systems in production environments.

You’ll navigate ambiguous problems, own end-to-end delivery from architecture to deployment, and drive impact across multi-turn agent workflows and RAG-enabled applications.

Qualifications

• Strong Python development experience with production systems.
• Experience designing and implementing ML pipelines and data workflows.
• Familiarity with LLMs, multi-turn workflows, and agent frameworks.
• Understanding of data quality, taxonomy design, and dataset curation for AI.

Responsibilities

• Work with AI labs and enterprise partners to define goals and technical requirements.
• Build data intelligence systems for collecting, organizing, evaluating, and improving data.
• Implement ML pipelines for data curation, training, evaluation, and experimentation.
• Develop LLM applications, including multi-agent systems and RAG workflows.
• Partner with research and engineering teams to translate AI problems into scoped projects.
• Develop infrastructure for model inference, evaluation, and deployment across platforms.
• Build systems that enable repeatable, scalable AI experiments and partner success.

Skills

Python

LLM Systems

ML Infrastructure

RAG/AI Automation

Job description

Job Title: Forward Deployed Engineer

Job Type: Full-time

Location: Remote / Travel-required

The Role

We’re hiring a Forward Deployed Engineer to work directly with the world’s leading AI labs and enterprises as a technical research and implementation partner. This role sits at the intersection of applied AI, ML infrastructure, data intelligence, and partner-facing product development.

You’ll help strategic partners define research directions, structure and curate high-quality data, implement ML and evaluation pipelines, and build the agentic systems that extend multi-turn agents and workflows in production. You should be comfortable moving between ambiguous research questions, technical architecture, hands-on engineering, and partner-facing execution.

Required Skills

• Python
• LLM Systems
• ML Infrastructure
• RAG/AI Automation

What You’ll Work On

• 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.

What We’re Looking For

• 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.

Preferred Qualifications

• 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.