[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-stem-jobs-in-india":3},{"Ques":4,"Slug":31,"Header":32,"job_category":62},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28],{"A":8,"Q":9},"Yes. The role is explicitly Remote and designed for distributed collaboration while supporting AI\u002FML training pipelines.","Are these STEM jobs in India remote?",{"A":11,"Q":12},"Yes. Employment Type is FULL_TIME.","Is this a full-time role?",{"A":14,"Q":15},"Typical work includes data labeling, RLHF preference ranking, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, and content safety labeling to improve training data quality.","What kind of work is involved day to day?",{"A":17,"Q":18},"Mid-Senior.","What experience level is targeted?",{"A":20,"Q":21},"Projects may span NLP, computer vision, content safety, and LLM evaluation tasks tied to large language model training pipelines.","What domains will I work on?",{"A":23,"Q":24},"Through annotation guidelines compliance checks, calibration sessions, audits, adjudication, and structured QA evaluation rubrics.","How is quality enforced?",{"A":26,"Q":27},"Rex.zone supports teams across AI labs, technology startups, BPO partners, and annotation vendors.","What employers does Rex.zone support for these roles?",{"A":29,"Q":30},"Emphasize engineering rigor, data labeling, RLHF, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, and experience improving training data quality.","What skills should I highlight in my application?","stem-jobs-in-india",{"desc":33,"title":34,"content":35},"STEM Jobs in India at Rex.zone focus on engineering and applied AI\u002FML workflows that power large language model training pipelines, RLHF evaluation, data labeling, and QA evaluation. In this full-time remote role, you will support training data quality, annotation guidelines compliance, and model performance improvement across NLP, computer vision, and content safety labeling. You will collaborate with distributed teams and vendors to deliver prompt evaluation, named entity recognition, and structured annotations that enable reliable AI systems. Explore and apply on Rex.zone to join technology projects serving AI labs, tech startups, and annotation vendors.","STEM Jobs in India",[36,38,41,44,47,50,53,56,59],{"h2":34,"desc":37},"Date: 25-02-2026\nCompany: Rexzone\nCountry: US\nRemote Type: Remote\nEmployment Type: FULL_TIME\nExperience Level: Mid-Senior\nIndustry: Technology\nJob Function: Engineering\nSkills: STEM, Engineering, Data Labeling, RLHF, QA Evaluation, Prompt Evaluation, Named Entity Recognition, Computer Vision Annotation, Content Safety Labeling, LLM Training Pipelines\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":39,"desc":40},"About the Role","You will work on STEM-aligned engineering tasks that connect directly to AI\u002FML production workflows: data labeling, RLHF, and large language model evaluation. You will apply annotation guidelines, perform QA checks, and improve training data quality across text, image, and multimodal datasets. You will contribute to prompt evaluation, rubric-based scoring, and content safety labeling to reduce risk and increase model reliability. You will document edge cases, calibrate reviewer agreement, and support model performance improvement through consistent, auditable outputs.",{"h2":42,"desc":43},"What You Will Do","Responsibilities include: (1) Execute data labeling tasks for NLP and computer vision annotation using clear taxonomies and instruction sets, (2) Perform RLHF-style ranking, preference labeling, and rubric scoring for LLM responses, (3) Run QA evaluation for completeness, consistency, and annotation guidelines compliance, (4) Conduct prompt evaluation and error categorization to surface failure modes, (5) Perform named entity recognition and span labeling with strict boundary rules, (6) Support content safety labeling for policy categories such as harassment, self-harm, and regulated goods, (7) Maintain training data quality through sampling plans, adjudication notes, and reviewer calibration, (8) Communicate findings to cross-functional stakeholders to drive model performance improvement.",{"h2":45,"desc":46},"Required Qualifications","You have a STEM background or equivalent engineering experience and can follow detailed specifications with high accuracy. You can reason about edge cases, ambiguity, and evaluation criteria. You have experience with QA evaluation, data labeling, or systematic review workflows. You can write clear documentation and apply consistent judgment across large volumes of items. You are comfortable working remotely in a structured, metrics-driven environment.",{"h2":48,"desc":49},"Preferred Qualifications","Experience with RLHF, prompt evaluation, LLM evaluation, or ranking tasks. Familiarity with named entity recognition, text classification, or computer vision annotation. Exposure to content safety labeling and policy enforcement categories. Experience improving training data quality via audits, inter-annotator agreement, and guideline refinement. Comfort collaborating with AI labs, tech startups, BPOs, and annotation vendors.",{"h2":51,"desc":52},"Tools and Workflows","You may work in web-based annotation tools, spreadsheet-based QA workflows, and issue-tracking systems. You will follow versioned guidelines, maintain audit trails, and participate in calibration sessions. You will use structured rubrics for QA evaluation and RLHF preference labeling, and you will contribute to workflow improvements that increase throughput without sacrificing training data quality.",{"h2":54,"desc":55},"How Success Is Measured","Key signals include annotation accuracy, consistency, and annotation guidelines compliance; training data quality metrics from audits; agreement rates during calibration; turnaround time; and the impact of QA evaluation findings on reducing recurring errors. Strong performance is reflected in measurable model performance improvement outcomes tied to reliable, well-documented labeling decisions.",{"h2":57,"desc":58},"Why Rex.zone","Rex.zone connects candidates to remote, full-time opportunities supporting real AI\u002FML systems. This role provides exposure to LLM training pipelines, RLHF, data labeling, and QA evaluation across multiple domains such as NLP, computer vision, and content safety labeling. You will work with distributed teams supporting AI labs, tech startups, BPO partners, and annotation vendors.",{"h2":60,"desc":61},"Apply","Apply through Rex.zone with a resume highlighting STEM projects, engineering experience, and any exposure to data labeling, RLHF, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, or content safety labeling. Include examples of guideline-based work, audit processes, or structured evaluation tasks that demonstrate strong judgment and attention to detail.","Engineering"]