Remote STEM Engineer; India — AI​/ML Data Operations

About The Role

Remote STEM Jobs in India e focus on applied engineering work that supports AI/ML training pipelines, including data labeling, RLHF evaluation, prompt evaluation, and QA evaluation for large language models. You will collaborate with globally distributed teams to produce high-quality training data and improve model performance across NLP, computer vision, and content safety labeling workflows.

• Design and execute annotation and evaluation workflows for NLP, computer vision, and multimodal tasks
• Implement RLHF and prompt evaluation protocols using well-defined rubrics and calibration
• Define and enforce annotation guidelines compliance; maintain edge‑case documentation
• Run QA evaluation audits, spot checks, inter‑annotator agreement analysis, and error taxonomy tracking
• Curate datasets for named entity recognition, classification, ranking, summarization, and safety policy adherence
• Use Python and data tooling to automate checks, sampling plans, reporting, and quality metrics

• Mid‑senior STEM/engineering background (CS, EE, math, physics, statistics, or related)
• Strong Python skills; comfort with structured and unstructured data
• Practical understanding of LLM evaluation, data labeling, and QA evaluation concepts
• Experience with NLP tasks (e.g., NER, text classification, relevance ranking, summarization)
• Ability to interpret guidelines, resolve ambiguity, and communicate asynchronously in a remote setting

• Experience with RLHF pipelines, preference ranking, or rubric‑based evaluation
• Familiarity with computer vision annotation (bounding boxes, polygons, segmentation)
• Experience building quality systems (sampling, audits, calibration sessions, disagreement resolution)
• Exposure to content safety labeling or trust & safety workflows

• Competitive hourly base pay: $30–$50 per hour

Visit Rex.zone and search for “Remote STEM Jobs in India” to find matching roles and apply. Highlight STEM engineering impact, Python/data tooling, and experience with data labeling, RLHF, LLM evaluation, training data quality, and annotation guidelines compliance.