Remote STEM Engineer (India) — AI/ML Data Operations

About The RoleRemote STEM Jobs in India at Rex.zone 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.Key ResponsibilitiesDesign and execute annotation and evaluation workflows for NLP, computer vision, and multimodal tasksImplement RLHF and prompt evaluation protocols using well-defined rubrics and calibrationDefine and enforce annotation guidelines compliance; maintain edge-case documentationRun QA evaluation audits, spot checks, inter-annotator agreement analysis, and error taxonomy trackingCurate datasets for named entity recognition, classification, ranking, summarization, and safety policy adherenceUse Python and data tooling to automate checks, sampling plans, reporting, and quality metricsRequired QualificationsMid-senior STEM/engineering background (CS, EE, math, physics, statistics, or related)Strong Python skills; comfort with structured and unstructured dataPractical understanding of LLM evaluation, data labeling, and QA evaluation conceptsExperience with NLP tasks (e.g., NER, text classification, relevance ranking, summarization)Ability to interpret guidelines, resolve ambiguity, and communicate asynchronously in a remote settingPreferred QualificationsExperience with RLHF pipelines, preference ranking, or rubric-based evaluationFamiliarity 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 workflowsCompensationCompetitive hourly base pay: $30–$50 per hourHow To ApplyVisit 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.