<p>We are looking for talented individuals to join our team in 2027. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at our Company. Successful candidates must be able to commit to an onboarding date by end of year 2027. Please state your availability and graduation date clearly in your resume. Team Introduction: The Applied Machine Learning Ark team combines system engineering and machine learning to develop and operate Large Language Model (LLM) service platforms that offer businesses Model-as-a-Service (MaaS) solutions, serving both large model providers and downstream users. The US team drives the design, development, and operation of MaaS solutions across the US and international markets outside mainland China. We are building full-stack, end-to-end solutions spanning text and multimodal LLM algorithms, LLM training/fine-tuning/inference frameworks, prompt engineering, model alignment, and intelligent agent systems. Beyond model serving, we operate large-scale log analytics pipelines that process massive volumes of invocation logs from text models, multimodal models, and agent systems - extracting usage patterns, quality signals, and actionable insights to inform model improvement, system optimization, and product decisions through continuous, data-driven feedback loops. We are actively seeking talented engineers and researchers specializing in Large Language Models and AI Agent systems to join our dynamic team. Topic Content: With foundation models gradually being applied in real ToB scenarios, AI system optimization now extends beyond the foundation model itself to include a complex business system composed of the model, prompt, memory, tools, skills, workflow, and the external environment. Compared to offline benchmarks, real-world cases offer greater potential for optimization but also present challenges such as larger data volumes, higher noise levels, more diverse scenarios, greater structural heterogeneity, and limited user feedback, making them difficult to be directly utilized. Relying on the real-world data accumulated on the Volcano Ark case platform, this project aims to unify logs, cases, feedback, and environmental information into structured objects that are understandable, and attributable, and optimizable. By integrating AI-assisted tools to guide users in providing efficient feedback, it aims to build an AI data flywheel system tailored to real scenarios. This system will both support foundation model iteration and address issues related to environment, memory, tools, and workflows within the business system, focusing on developing agent optimization capabilities that enhance SA/FDE's efficiency in supporting customers. Responsibilities: - Building a next-generation big model as a service platform to serve hundreds of LLMs based applications; - To develop and maintain the big model as a service platform, including offline training/finetuning, online inference, model management, and resource orchestration, etc.; - To manage a huge number of GPU resources and provide computing power efficiently.</p> <p>Minimum Qualifications: - Currently pursuing or recently completed a Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related technical field. - Research experience in one or more of the following areas: LLM post-training and alignment, model evaluation, test-time scaling, agent systems, or large-scale data curation and optimization. - Demonstrated research ability through publications, substantial research projects, or internships. - Ability to work independently on open-ended research problems, from problem formulation to experimental execution. Preferred Qualifications: - Strong interest in foundation models and data-centric AI, particularly in how large models can improve over time through better data, feedback, and system design. Relevant directions include data flywheels, continual learning, data curation and valuation, and the co-design of algorithms and infrastructure. - A strong publication record with multiple first-author papers, in areas of machine learning, NLP, data mining, or related fields. - Internship or research experience in similar fields, ideally with experience with scalable ML systems, especially those involving real-world deployment, feedback loops, or human-in-the-loop pipelines. - Strong motivation to connect research with practice, and to build end-to-end AI systems spanning modeling, data, evaluation, and infrastructure.</p>