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 us for an internship.
Our internship program offers students hands-on experience, industry exposure, and opportunities to apply their knowledge to real-world challenges while building a strong foundation for personal and professional growth.
Interns will gain practical experience, explore potential career paths, and participate in social events, learning programs, and development workshops alongside industry professionals.
Candidates may apply to a maximum of two positions across Our Company and its affiliates globally. Applications will be considered in the order they are submitted.
Applications are reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume, including your start and end dates.
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.