Summary
✨ AI‑Generated
A remote engineering opportunity focused on designing and scaling systems that support advanced AI model training workflows. The role involves building reliable data pipelines, evaluation platforms, automation tools, and quality systems while collaborating with cross-functional technical teams.
Highlights
Remote engineering role focused on building scalable AI training infrastructure, automation workflows, and systems that improve data quality and operational efficiency.
Description
Rex.zone is hiring for remote, full-time engineering roles focused on building, testing, and scaling systems that support real-world AI/ML training workflows, including RLHF, data labeling, QA evaluation, prompt evaluation, and LLM training pipelines.
About The Role
You will design and implement end-to-end engineering solutions connecting dataset ingestion, labeling operations, preference data collection, evaluation pipelines, and quality assurance controls.
You will partner with ML, Data Ops, and Product to translate annotation guidelines into scalable systems and ship reliable services that improve training data quality and throughput.
Key Responsibilities
Build and maintain data pipelines for labeled data, preference data, and evaluation datasetsDevelop internal tools for RLHF, prompt evaluation, and QA evaluationImplement workflow automation for annotation guidelines compliance, audit trails, and reviewer consensusIntegrate data labeling platforms and content safety labeling into ML pipelinesDefine monitoring for data quality, latency, cost, and model evaluation metricsCollaborate with NLP and computer vision teams to support NER, classification, and CV annotation tasksHarden systems for security, privacy, and access control in distributed remote teams
Required Qualifications
Mid-senior engineering experience building production systemsStrong programming and systems fundamentals with experience designing scalable servicesExperience with data engineering concepts (ETL/ELT, schemas, versioning, lineage)Familiarity with ML pipelines, dataset management, and evaluation workflowsAbility to work cross-functionally with data labeling and QA teamsStrong written communication for requirements, runbooks, and incident retrospectives
Preferred Qualifications
Experience supporting LLM training pipelines, RLHF tooling, and prompt evaluation frameworksExperience building quality systems for annotation guidelines compliance and adjudicationFamiliarity with MLOps practices, CI/CD, and observability for data-intensive systems
Compensation
Competitive hourly rate: $30–$50 per hour.
How To Apply
Apply via Rex.zone and highlight experience with scalable system design, data pipelines, and any RLHF, data labeling platforms, QA evaluation, or LLM training pipeline work.
