AI Developer

AI Developer Jobs in the United States (Remote, Full Time)

Rex.zone is hiring AI Developers to build and ship production AI systems and LLM training workflows end to end—spanning data ingestion, feature pipelines, model integration, evaluation, deployment, and monitoring.

You will design and deliver AI/ML features that connect training data to model behavior in production. You will implement LLM application patterns (RAG, tool use, structured outputs), build evaluation harnesses, and collaborate on feedback loops (including RLHF-style workflows) to improve quality, reliability, and safety.

• Build and maintain AI services (batch and real‑time) and integrate model inference APIs
• Develop data pipelines for training and evaluation (versioning, validation, quality checks)
• Create automated evaluation for LLMs, prompt quality, and safety regressions using offline metrics and human‑in‑the‑loop review
• Support NLP/CV workflows such as classification, summarization, ranking, NER, and content moderation
• Document experiments and promote reproducible ML engineering practices

• Mid‑senior experience building AI/ML‑powered products in production
• Strong Python and software engineering fundamentals (APIs, testing, CI/CD, code reviews)
• Hands‑on ML/DL knowledge with exposure to LLMs, NLP, or computer vision
• Ability to design evaluation plans and use results to drive model improvement
• Comfort with data labeling workflows, QA evaluation, and annotation guideline compliance

• Experience with RLHF, preference modeling, prompt iteration, or human feedback loops
• Familiarity with MLOps deployment, monitoring, and experiment tracking
• Experience with retrieval‑augmented generation, vector search, and embedding pipelines
• Background in content safety labeling, red‑teaming, or policy‑aligned evaluation

Competitive hourly base pay: $30–$50 per hour, depending on experience and scope.

Apply via Rex.zone with a resume highlighting shipped AI systems, evaluation results, and any experience with RLHF, labeling workflows, or QA evaluation. Include links to repositories, case studies, or technical write-ups where possible.