Remote | Forward Deployed Engineer — $300,000–$650,000/year

24-Mag•New York, NY

6h•Remote

About The Position

This full-time, remote opportunity is for an experienced Forward Deployed Engineer working across applied AI, machine-learning infrastructure, data intelligence, and partner-facing technical implementation. The role involves working directly with AI research teams and enterprise partners to translate complex or ambiguous AI problems into scoped technical projects and production systems. Work spans research collaboration, ML and evaluation infrastructure, large-scale data systems, LLM applications, and hands-on engineering from discovery and architecture through deployment, iteration, and partner success. Travel may be required as needed.

Requirements

Strong professional experience as a software, machine-learning, applied AI, or infrastructure engineer.
Advanced Python engineering skills and experience building and shipping production systems end to end.
Practical experience with LLMs, agentic systems, multi-turn workflows, tool use, RAG, or AI automation.
Experience building or maintaining data pipelines, ML infrastructure, model-evaluation systems, or research workflows.
Strong understanding of data quality, taxonomy design, labelling workflows, and dataset curation for AI systems.
Ability to work independently in ambiguous, partner-facing environments with strong technical and product ownership.
Comfort working directly with researchers, technical partners, founders, and enterprise stakeholders.
Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party.

Nice To Haves

Experience within a startup, AI infrastructure company, applied AI organisation, or research-focused engineering team.
Experience building multi-turn agents, agent-evaluation systems, workflow automation, or human-in-the-loop AI.
Familiarity with modern LLM tooling, agent frameworks, model-evaluation stacks, and ML experimentation platforms.
Experience acting as a technical partner to external customers, research teams, or strategic enterprise accounts.

Responsibilities

Work with AI research teams and enterprise partners to define research goals, technical requirements, and project direction.
Translate ambiguous AI and machine-learning problems into clearly scoped technical projects.
Act as a technical implementation partner across research, engineering, product, and stakeholder teams.
Move between research questions, technical architecture, hands-on engineering, and partner-facing execution.
Own technical systems from discovery through implementation, deployment, iteration, and ongoing reliability.
Build large-scale data-intelligence systems for collecting, organising, evaluating, and improving training and evaluation data.
Implement ML pipelines for data curation, model training, evaluation, experimentation, and continuous improvement.
Develop infrastructure for model inference, experimentation, evaluation, and deployment.
Design reliable workflows for advanced AI research and production environments, and maintain systems supporting complex data and machine-learning workloads.
Develop LLM applications, including multi-agent systems, tool-using agents, RAG workflows, and human-in-the-loop systems.
Build evaluation harnesses and infrastructure for assessing agent and model behaviour.
Develop reliable, repeatable, multi-turn workflows from one-off AI experiments.
Implement agentic automation across technically complex business and research processes.
Apply modern LLM tooling and model-evaluation approaches to production-oriented AI systems.
Design data taxonomies, labelling systems, evaluation rubrics, and quality frameworks.
Improve dataset structure and quality to support model performance and research outcomes.
Develop workflows for dataset curation, annotation, validation, and quality assurance.
Analyse data and model behaviour to identify opportunities for system improvement.
Apply rigorous standards to training data, evaluation datasets, and research workflows.