Role Description Are you curious, detail-oriented, and excited about shaping the future of artificial intelligence? We’re looking for AI Evaluation & Annotation Specialists to help train and improve Large Language Models (LLMs). In this role, you’ll review AI-generated responses, provide corrections, evaluate quality, and follow structured guidelines to ensure accuracy and consistency. No engineering background required — if you enjoy problem-solving, analyzing language, and following structured tasks, this role is a great fit. This is a hands-on, production-based role where accuracy, focus, and consistency matter. What You’ll Do - Review AI-generated responses and rate them for clarity, correctness, and relevance. - Annotate and label content based on project-specific guidelines. - Follow detailed written instructions and apply them consistently. - Generate or evaluate prompts depending on assignment type. - Work with QA Leads to apply feedback and continuously improve task quality. - Report completed work daily and meet productivity and quality standards. Qualifications - You’re detail-oriented and enjoy accuracy-based work. - You can follow instructions carefully and apply them consistently. - You’re comfortable working independently with minimal supervision. - You have strong reading comprehension and critical thinking skills. - You communicate clearly and respond to feedback professionally. - Experience with annotation, evaluation, translation, linguistics, or QA is helpful, but not required — training and guidance is provided. Requirements - Fluency in Korean, with strong written and verbal communication skills. - Bachelor's degree in Linguistics, Computer Science, or a related field; or equivalent experience. - Experience or interest in AI, Machine Learning, or data annotation preferred. - Strong attention to detail and ability to work with complex data sets. - Familiarity with translation and localization processes is an advantage. - Ability to work independently and collaboratively in a fast-paced environment. - Proficient in using Artificial Intelligence and data annotation tools, with a willingness to learn new technologies. Benefits - Work with a global team. - Entry point into the growing AI and language technology industry. - Exposure to real-world AI model training. - Skill development in annotation, QA/evaluation, and structured AI tasks. Compensation Range Rates vary by language and experience level (L1/L2). Below are current approved ranges in USD: - $11.00 USD to $14.00 USD per hour. Assessment Requirement All applicants are required to complete a short skills-based assessment as part of the selection process. This assessment is unpaid and is used solely to confirm eligibility and alignment with project quality standards. Completing the assessment does not guarantee selection; however, it is mandatory in order to be considered for this role.

• Provide quality instruction in each assigned course within the approved academic program curriculum. • As appropriate, the Dean/Department Chair may solicit assistance in the development, implementation, and continuous evaluation of the curriculum, including sequence of courses and student learning outcomes. • Should respond, in a timely manner, to specific and general information requests from the institution and administrative officials, prospective employers, professional organizations, public agencies, civic organizations, private foundations, general public, and students, as appropriate. • At all times, promote appropriate standards of linguistic expression in both written and oral communications. • Ensure that all academic program requirements and forms of documentation (e.g., clinical evaluations, competency documentation) are completed as required for each student and submitted per established deadlines. • Ensure all faculty expectations are met on a weekly basis. • Appropriately manage all classroom activities. • Be reasonably accessible to students for questions and assistance. • Monitor educational and professional literature for the best practices in areas related to courses taught. • Assist with the development of curricular and policy changes as requested by the Dean/Department Chair. • Assist with the selection of new textbooks as appropriate. • Adjunct (part-time) faculty members teaching on a regular basis are required to participate in professional development activities – minimum of 1 per year.

• Prowadzeniem kompleksowego procesu sprzedażowego B2B – od pierwszego kontaktu, przez analizę potrzeb biznesowych, aż po przekazanie klienta do zespołu wdrożeniowego (Customer Success) • Obsługą dostarczonej bazy leadów – Twoim zadaniem będzie kontakt z potencjalnymi klientami zgodnie z naszym standardem sprzedaży doradczej • Kluczowe w naszym procesie jest zrozumienie wyzwań, z jakimi mierzą się placówki i zaproponowanie najlepszego rozwiązania • Budowaniem relacji z decydentami – menedżerami, właścicielami, dyrektorami medycznymi i członkami zarządów • Prowadzeniem rozmów sprzedażowych telefonicznie, mailowo oraz w formie spotkań online • Analizą celów i wyzwań placówek medycznych, by jak najlepiej dopasować naszą ofertę • Prezentowaniem ofert dostosowanych do potrzeb różnych struktur organizacyjnych • Realizacją celów sprzedażowych, opartych na wartości kontraktu, liczbie wdrożeń i aktywowanych użytkowników • Ścisłą współpracą z zespołami Customer Success, Product i Marketingu, by zapewnić płynne wdrożenie i długofalową satysfakcję klienta

• Define and champion the overall AI strategy and roadmap in alignment with the company's product vision and business goals. • Lead the selection and integration of advanced machine learning models, algorithms, and deep learning techniques relevant to embodied AI (e.g., perception, manipulation, natural language understanding). • Foster a culture of innovation, rigorous research, and responsible AI development that supports the team's ongoing success. • Ensure that all AI systems adhere to the highest standards of safety, privacy, and ethical guidelines. • Partner with Hardware and Robot Software leadership to ensure the sensor suite and embedded compute are optimized for current and future AI model architectures. • Oversee the design, development, and deployment of on-robot and cloud-based AI systems, leveraging the existing team's expertise. • Establish MLOps best practices for efficient model training, deployment, monitoring, and iteration. • Drive research into novel AI solutions for complex robotics challenges, such as real-time planning, human-robot interaction, and adaptive motor control. • Deliver production-ready AI models that meet performance, latency, and reliability requirements for commercial deployment. • Spearhead the Sim2Real strategy, utilizing physics-based simulation (e.g., MuJoCo, Isaac Sim) to train robust policies that transfer seamlessly to hardware. • Accelerate our data flywheel strategy to combine teleoperation, synthetic data, and fleet learning to expand the fleet performance and reliability. • Champion the existing team's talent and represent their needs to senior leadership. • Develop a comprehensive hiring plan to staff and scale the AI team, including defining required roles (e.g., ML Engineers, Data Scientists, AI Researchers). • Lead recruitment, mentorship, and career development for all members of the AI team. • Manage the budget allocated for team salaries, equipment, and necessary infrastructure. • Forecast and manage the budget for cloud computing resources (GPUs, TPUs) necessary for large-scale model training and experimentation. • Secure and allocate funding for specialized datasets and data annotation services. • Evaluate and procure necessary software licenses and tools for AI development and simulation. • Regularly report on expenditure and resource utilization to senior leadership.