Data Lake Infrastructure & Data Analytics Research Engineer Graduate (AML-Ark-US) - 2027 Start (PhD)

• Build and maintain the data lake that powers model and agent development, including storage design, query performance, and cost efficiency.
• Develop large-scale batch and streaming pipelines that turn raw model and agent logs into analysis-ready datasets.
• Build analytics and metrics platforms that let algorithm and product teams find actionable insights quickly.
• Own data quality and governance, and partner with algorithm teams to support the data flywheel behind continuously improving systems.

• Individuals who are completing or have recently completed a PhD's in Computer Science, Software Engineering, Data Science, or a related field.
• Solid fundamentals in data structures, algorithms, and distributed systems.
• Strong programming skills in at least one mainstream language, plus proficiency in SQL.
• Hands-on experience with a big data engine or lakehouse storage system.
• Demonstrated ability through substantial projects, internships, research, or open-source work.

• Understanding of query engine and lakehouse internals, including columnar storage and query optimization.
• Experience with streaming pipelines or OLAP systems.
• Experience analyzing LLM or agent logs and traces.
• Experience with cloud-native infrastructure and performance tuning at scale.

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.