Forward Deployed Engineer | Remote

Crossing Hurdles; United States; On-site; USD 110,000 - 180,000; Full time

Job summary

Crossing Hurdles is seeking a Python-focused ML engineer to collaborate with leading AI labs and enterprise partners on research goals and technical requirements.

You will build large-scale data intelligence systems for collecting, organizing, evaluating, and improving training and evaluation data, and implement ML pipelines for data curation, model training, evaluation, experimentation, and continuous improvement.

Qualifications

• Proven experience building and shipping production AI systems end to end.
• Experience with LLMs, agentic systems, multi-turn workflows, 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.
• Able to work directly with technical partners, researchers, founders, and enterprise stakeholders.

Responsibilities

• Define research goals, technical requirements, and project direction with partners.
• Build data intelligence systems to collect, organize, evaluate, and improve training data.
• Implement ML pipelines for data curation, model training, evaluation, experimentation, and continuous improvement.
• Design data taxonomies, labeling systems, and quality frameworks to improve datasets and models.
• Develop LLM applications including multi-agent systems, tool-using agents, and RAG workflows.
• Own the systems lifecycle from discovery to deployment and partner success.

Skills

Python engineering

LLMs experience

Data pipelines

Dataset curation

Taxonomy design

Labeling workflows

Stakeholder communication

Job description

• 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.
• Own systems across the full lifecycle, including discovery, architecture, implementation, deployment, reliability, iteration, and partner success.

Requirements

• Have strong relevant experience in Python engineering with a proven track record of building and shipping production systems end to end.
• Have experience working with LLMs, agentic systems, multi-turn workflows, tool use, RAG, or AI automation.
• Have built or maintained data pipelines, ML infrastructure, evaluation systems, or research workflows.
• Possess a strong understanding of data quality, taxonomy design, labeling workflows, and dataset curation for AI systems.
• Be comfortable working directly with technical partners, researchers, founders, and enterprise stakeholders.