Agent Evaluation and Evolution Machine Learning Engineer Graduate - Applied Machine Learning Ark

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. We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume. Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early. Responsibilities: - Design evaluation systems for LLM-based agents, covering task success, tool use, reasoning quality, and reliability. - Build benchmarks and automated judging pipelines, combining rule-based checks, model-based judging, and human review, etc. - Analyze agent execution traces and user feedback to identify failure patterns and turn them into concrete system improvements. - Support the closed loop from experience to capability, and work with research, platform, and product teams to bring methods into production. Minimum Qualifications: - Individuals who are completing or have recently completed a Bachelor's/ Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field. - Solid foundation in machine learning and deep learning. - Hands-on experience with LLM-based systems (e.g., agents, tool calling, retrieval, multi-agent systems) through research, internships, or projects. - Strong Python skills and experience with a mainstream ML or agent evaluation framework. - Demonstrated research or engineering ability through publications, substantial projects, internships, or open-source work. Preferred Qualifications: - Publications at top-tier ML/NLP venues (e.g., NeurIPS, ICML, ICLR, ACL etc.), especially in agent learning, self-improving/self-evolving/RSI, or agent evaluation. - Experience with evaluation methodology: metric design, model-based judging, or annotation and statistical analysis, etc. - Familiarity with LLM post-training, reasoning and planning methods, or continual learning. - Experience with feedback-driven optimization loops, or with large-scale log and trace analysis.