• Assist in documenting customer GenAI use cases using established templates and value frameworks • Support customer discussions on AI value, readiness, and adoption under guidance from senior team members • Help gather and organize customer requirements, success criteria, and outcomes • Maintain structured documentation of approved use cases and adoption milestones • Support account teams with preparation of value narratives, summaries, and internal materials • Assist in organizing and preparing customer workshops, demos, and meetings • Capture field observations and feedback and escalate themes to senior stakeholders • Help maintain internal reference materials and best-practice documentation • Support tracking of AI initiatives, timelines, and deliverables • Maintain program artifacts including trackers, notes, and adoption summaries • Assist with coordination across AI Technical Specialists, Sales, and Customer Success teams • Ensure updates and deliverables are completed accurately and on time • Work closely with AI Technical Specialists to align business context with technical execution • Partner with Customer Success teams to support adoption activities and value tracking • Contribute structured inputs to Product and GTM teams through defined feedback channels

• Architect a comprehensive "Quality Intelligence" platform using generative AI to predict defect hotspots, intelligently optimize regression suites, auto-generate tests, and enable self-healing automation. • Define enterprise-wide AI-first testing strategy, including non-deterministic evaluation paradigms, continuous monitoring for drift/hallucination, and integration across the full SDLC. • Establish governance for ethical AI testing, aligning with emerging standards. • Design and implement advanced benchmarks, red teaming protocols, and adversarial testing for internal AI agents and generative features—focusing on hallucination rates, bias/fairness, prompt injection, jailbreaks, and goal misalignment. • Build evaluation pipelines with statistical rigor (e.g., multi-trial runs, LLM-as-judge, human-in-the-loop) using tools like LangFuse, LangSmith, DeepEval, RAGAS, or Arize Phoenix for metrics such as faithfulness, context precision, and safety compliance. • Partner with DevOps to embed AI-based testing into GitHub-based CI/CD pipelines (e.g., AI-generated tests, predictive flakiness detection, automated gating with quality signals). • Lead design of self-healing test frameworks (integrating AI plugins with Playwright/Cypress or similar) that adapt to UI/model changes with minimal maintenance. • Architect synthetic data generation, maintain golden data-sets and AI-powered data masking solutions to enable privacy-compliant, high-fidelity testing at scale. • Collaborate with product, data science, ML engineering, and security teams to influence AI feature design with quality guardrails from day one. • Evangelize and mentor: Upskill traditional QA engineers into AI-augmented testers through workshops, playbooks, and communities of practice. • Drive adoption of AI quality best practices organization-wide, including metrics dashboards for DORA + AI-specific indicators (e.g., hallucination rate, red team success rate, self-healing coverage).

• Collaborate with the AI team on innovative projects • Participate in the development of AI models • Support the implementation of AI solutions in Biogen's processes • Engage with cross-functional teams to drive initiatives

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.