Data Lake Infrastructure & Data Analytics Research Engineer Graduate

About the Role

The Applied Machine Learning Ark team develops and operates Large Language Model (LLM) service platforms offering Model-as-a-Service (MaaS) solutions. The US team leads the design, development, and operation of MaaS solutions for US and international markets outside mainland China. The team is building full-stack, end-to-end solutions including LLM algorithms, training/fine-tuning/inference frameworks, prompt engineering, model alignment, and intelligent agent systems. Beyond model serving, the team operates large-scale log analytics pipelines for text models, multimodal models, and agent systems to extract usage patterns, quality signals, and insights for model improvement, system optimization, and product decisions.

Key Responsibilities

• Build and maintain the data lake for model and agent development, focusing on storage design, query performance, and cost efficiency.
• Develop large-scale batch and streaming pipelines to transform raw model and agent logs into analysis-ready datasets.
• Construct analytics and metrics platforms to enable algorithm and product teams to quickly find actionable insights.
• Own data quality and governance, partnering with algorithm teams to support the data flywheel for continuously improving systems.

Requirements & Tech Stack

• 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.
• 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.

Preferred Qualifications

• 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.