Data Lake Infrastructure & Data Analytics Research Engineer Graduate

About the Role

The Applied Machine Learning Ark team combines system engineering and machine learning to develop and operate Large Language Model (LLM) service platforms offering Model-as-a-Service (MaaS) solutions. The US team focuses on the design, development, and operation of MaaS solutions across US and international markets outside mainland China. The team builds 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, the team operates large-scale log analytics pipelines to extract usage patterns, quality signals, and actionable 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.
• Build analytics and metrics platforms to enable algorithm and product teams to find actionable insights quickly.
• Own data quality and governance, partnering with algorithm teams to support the data flywheel for continuously improving systems.

Requirements & Tech Stack

• 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.

Qualifications

Minimum Qualifications:

• Individuals completing or recently completed a Bachelor's/Master's in Computer Science, Software Engineering, Data Science, or a related field.
• 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.

Compensation

• Base salary range: $128,000 - $256,000 annually.
• Potential eligibility for discretionary bonuses/incentives and restricted stock units.

Benefits

• Medical, dental, and vision insurance.
• 401(k) savings plan with company match.
• Paid parental leave.
• Short-term and long-term disability coverage.
• Life insurance.
• Wellbeing benefits.
• 10 paid holidays per year.
• 10 paid sick days per year.
• 17 days of Paid Personal Time (prorated upon hire).