字节跳动Conversational AI Large Model Researcher | 智能对话大模型算法工程师-电商-筋斗云人才计划

•Participate in the development of knowledge-based Q&A or conversational AI products, enhancing the capabilities of large language models in areas such as RAG (Retrieval-Augmented Generation) and Agents, including data systems, algorithm optimization, prompt engineering, and evaluation iteration. •Build a systematic and specialized knowledge base, optimize the full-link retrieval technology, and continuously improve retrieval precision and recall. •Improve the performance of large language models in Q&A and conversational scenarios through techniques such as RAG, SFT (Supervised Fine-Tuning), and RLHF (Reinforcement Learning from Human Feedback).

Product Strategy & Development * Own the product vision, strategy, and roadmap for AI-powered Seller Education experiences. * Identify and prioritize learning opportunities across the seller lifecycle (e.g., onboarding, growth, compliance, optimization). * Design and launch AI solutions (e.g., adaptive learning, content recommendation, conversational tutors) that improve learning outcomes and engagement. * Drive measurable improvements in Seller learning efficiency, satisfaction, and business impact. * Lead continuous product improvement using experiments, data insights, and qualitative feedback from Sellers and stakeholders. Technical Leadership * Partner with data science and engineering teams to design, build, and scale AI/ML and LLM-based learning features. * Define clear technical requirements, success metrics, and guardrails for AI-powered education workflows. * Oversee end‑to‑end product development cycles, from discovery and design through implementation and launch. * Promote data-informed decision making and experimentation across all product initiatives. Stakeholder & Program Management * Lead cross‑functional collaboration between product, program, tech, content, marketing, and operations teams. * Communicate product strategy, roadmap, and results to senior leadership, highlighting trade‑offs and impact. * Drive alignment and change management across CN and global teams to scale adoption of AI‑powered learning solutions. * Influence internal and external partners to co‑create high‑quality, localized, and relevant learning experiences for Sellers.

The ideal candidate will have experience in customer-facing roles and success in leading in-depth technical Application development architecture discussions with senior customer executives, Architects, IT Management, and Developers to drive value to our customers and is open to travel to customer site as needed by business. • Understanding Customer/Partner Technical Environment (Insights about Customer/Partner and Industry): Gather customer/partner insights (e.g., feedback around technical preferences, environments, business needs, competitive landscape), and map architecture and digital transformation solutions to customer/partner business outcomes. Adapt business models, plans, and solutions to insights. • Understanding Customer/Partner Technical Environment (Internal Advocacy): Act as the voice of the customer (VOC)/partner by driving new feedback, gaps, blockers, insights, resources, etc. across communities to track, add, and prioritize, using established channels (e.g., UAT/TFT). • Architecture Design and Deployment (Architecture Proposals): Receive and synthesize data about customer/partner business and technical requirements, address them with technical architecture(s), demonstrate and prove those solutions capability and business value through design collaboration sessions with the customer/partner. • Architecture Design and Deployment (Requirements and Constraints): Apply broad technical knowledge across various architecture solutions to meet business and information technology (IT) requirements and resolve identified technical constraints. Help to shape and enhance customers' requirements. • Application Development, Architecture Design and Deployment (Resolving Blockers): Identify, escalate, and work to resolve technical blockers (e.g., changing configurations, sample coding) to accelerate architecture implementations and routes non-technical issues for removal by the appropriate party. • Trusted Advisor (Challenger Mindset): Respectfully challenge customers/partners when going in the wrong direction and escalate appropriately. • Trusted Advisor (Competitor Insights/ Differentiated Value Proposition): Understand the competitor's architecture solutions and identify Microsoft's strengths over competitive solutions to drive conversations with customers/partners and convince them of solution. • Customer Usage: Lead architecture design, resiliency reviews, and technical optimization that result in production deployment application and increase customer business value. Drive efforts to ensure that the customer's environment and applications are well-architected. • Customer Satisfaction – Deliver positive Customer Satisfaction and become trusted advisors to customers by leveraging solution area expertise to enable defined Customer Success Plan outcomes.

We are seeking a skilled developer to build production-grade AI applications, focusing on LLM-based agents and tool-using systems. You will integrate large language models (LLMs), retrieval-augmented generation (RAG), and external tools/APIs on GPU-accelerated stacks, enhancing agent frameworks for reliability, scalability, and safety. What You’ll Be Doing: • Design, implement, and deploy AI-powered features using LLMs, including autonomous and multi-agent workflows. • Build agent toolchains, including planning, tool/function calling, memory management, RAG integration, and enterprise API connectivity. • Enhance agent frameworks with custom planners, routers, concurrency control, state management, and retry mechanisms. • Develop evaluation and observability systems to monitor agent performance (success rates, tool-call accuracy, latency, cost, traces). • Implement safety and compliance measures, including content filtering, PII handling, and policy enforcement using guardrail frameworks. • Optimize inference pipelines for GPU performance, latency, and cost; deploy via microservices and APIs. • Manage CI/CD, containerization, and deployment; maintain monitoring, logging, and alerting; and produce clear documentation.