携程Senior AI QC EN SHACC ALL(MJ035078)
任职要求
- Educational Background: Full-time bachelor's degree or higher.
- Work Experience: 3+ years of experience in internet product operations. Hands-on experience in LLM/AIGC product operations is highly preferred.
- Professional Skills:
- Mastery of AI evaluation methodologies (e.g., Golden Dataset construction, Human Evaluation SOPs).
- Sharp eye for detail ("bug hunting" mindset) with the ability to mine potential risks …工作职责
- AI Quality Assessment Framework & Standards - Establish and maintain quality evaluation criteria for LLMs, covering accuracy, safety, compliance, and instruction-following capabilities. - Design evaluation workflows combining automation and human-in-the-loop assessment to quantify model performance regularly. - End-to-End Quality Control & Risk Management - Own regression testing and acceptance testing for model iterations, enforcing quality gates to prevent performance degradation before launch. - Monitor online performance to mine for badcases, identifying model hallucinations, logic errors, or experience defects, and issue timely risk alerts. - Issue-Driven Improvement & Closed-Loop Management - Lead Root Cause Analysis for quality issues, precisely diagnosing whether failures stem from data, prompts, or model architecture. - Drive algorithm, product, and operations teams to resolve quality defects, track the fix rate and conduct re-validation to ensure a closed-loop process. - Cross-functional Collaboration - Bridge the gap between business needs and technical implementation, ensuring the AI delivers real value to users.
Define AI product vision and strategy across Global Selling seller segments and lifecycle stages; identify high-impact opportunities to apply AI/ML (e.g., GenAI, agentic AI, intelligent automation) to empower business teams — including Marketing, CRM, seller acquisition, and seller growth — to drive operational efficiency and business outcomes at scale. Own the end-to-end AI product lifecycle — from ideation and opportunity sizing, through technical development and experimentation, to launch, adoption, and iterative improvement — ensuring alignment between business objectives, user needs, and technological capabilities. Develop and communicate product requirements including model objectives, data specifications, evaluation metrics, and responsible AI guardrails; earn trust from cross-functional stakeholders (engineering, applied science, design, business operations) and external partners through clear documentation and aligned decision-making. Lead cross-functional and cross-regional collaboration with scientists, engineering, and data teams to build, evaluate, and ship AI-powered products; drive development velocity by managing trade-offs, unblocking technical dependencies, resolving issues, and maintaining rigorous experimentation standards (A/B testing, model performance benchmarks). Drive measurable business impact post-launch through continuous monitoring, data-driven iteration, and feedback loops; leverage market analysis and competitive intelligence to inform product positioning and roadmap evolution. Manage stakeholder and leadership communications across multiple geographies — including problem framing, AI solution trade-off analysis, product/business reviews, budget and headcount planning, and organizational change management for AI adoption at scale. Partner with international stakeholders, leaders, and tech teams to align priorities and drive execution across regions.
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. What you’ll be doing: • Use and develop AI-powered tools to make software testing smarter, faster, and more effective! • Improve test case generation, defect detection, flaky test analysis, regression testing, and test coverage optimization. • Work with product, engineering, and cross-functional teams to review requirements and define test strategies. • Build test plans, design and execute test cases, and report quality status, risks, bugs, and results. • Perform functional, performance, fault-injection, reliability, and regression testing for cloud-native systems. • Automate test cases and contribute to scalable test frameworks. • Manage the bug lifecycle, reproduce customer issues, and verify fixes.
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. What you’ll be doing: • Use and develop AI-powered tools to make software testing smarter, faster, and more effective! • Improve test case generation, defect detection, flaky test analysis, regression testing, and test coverage optimization. • Work with product, engineering, and cross-functional teams to review requirements and define test strategies. • Build test plans, design and execute test cases, and report quality status, risks, bugs, and results. • Perform functional, performance, fault-injection, reliability, and regression testing for cloud-native systems. • Automate test cases and contribute to scalable test frameworks. • Manage the bug lifecycle, reproduce customer issues, and verify fixes.