Remote LLM & Data Pipeline Engineer

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

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.