Data Annotator

Data Annotator - Detalhes da Vaga. ● Open major, provided the academic background demonstrates high-level critical thinking, synthesis, and reading comprehension (e. ●G. ●, Translation, Law, Psychology, Marketing, or Humanities). ●- C1 (Advanced) or C2 (Mastery) English fluency. ●- 3-4 months of data annotation or LLM evaluation experience (Textual data annotation preferred)- Proficiency in reading comprehension and possessive of strong writing skills, with the ability to understand and interpret complex and diverse texts. ●- Ability to work independently, follow instructions, manage time effectively, and meet deadlines in a dynamic and fast-paced environment. ●- Ability to manage ambiguity, uncertainty, and changing priorities with limited resources. ●- Ability to accept and integrate continuous feedback while remaining professional. ●- Excellent verbal and written communication skills and the ability to collaborate within a multi-disciplinary team. ●- Quick learner and self-starter with strong problem-solving skills. ●Nice to Have Skills & Experience:- Plusses:- Experience w RLHF (Reinforcement Learning Human Feedback)- Multiple Language proficiency- Experience with multi modal data annotation (Text, Image, Video)- QA or QC experienceJob Description:Their day to day includes accurately labeling machine learning textual data to refine AI models. ● This will primarily involve annotating large volumes of textual data according to predefined guidelines. ● This person must be able to read, write and summarize at a high volume and high accuracy. ● The Pre-screening tests typing speed (45-60wpm), reading comprehension and pattern recognition. ● Finding those who are sharp and can navigate ambiguous technical jargon would be a great fit for the role. ●Day to Day ResponsibilitiesAccurately label machine learning data through various methods to refine AI models. ● This will primarily involve annotating large volumes of textual data according to predefined guidelines. ●Collaborate with team members to identify improvements to the labelling interface or labelling processes in order to drive efficiency and high-quality. ●Complete assignments by deadlines while meeting team expectations and goals. ●. Requisitos para participar do processo. ● . O que oferecemos.