Graduate Data Lake & LLM Analytics Engineer

ByteDance; San Jose (CA); On-site; USD 162,000 - 317,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

401(k) with company match

Paid parental leave

Disability coverage (short/long term)

Life insurance

Wellbeing benefits

10 paid holidays per year

10 paid sick days per year

17 days Paid Personal Time

Job summary

ByteDance in San Jose, CA seeks engineers and researchers for the Applied Machine Learning Ark team to design and operate MaaS platforms across the US and beyond. You will work on end-to-end LLM/AI systems, from data lakes to analytics, shaping model and product decisions.

Successful candidates will contribute to large-scale log analytics, training/fine-tuning/inference frameworks, and intelligent agent systems while collaborating with cross-functional teams. Onboarding by year-end is required.

Qualifications

• PhD in CS/SE/DS or related field.
• Strong fundamentals in data structures, algorithms, and distributed systems.
• Proficiency in SQL and at least one mainstream programming language.
• Hands-on experience with a big data engine or lakehouse storage system.
• Proven project/research/open-source work.

Responsibilities

• Build and maintain data lake powering model and agent development, including storage design and cost efficiency.
• Develop large-scale batch and streaming pipelines to turn raw logs into analysis-ready datasets.
• Build analytics and metrics platforms enabling rapid insights for algorithm and product teams.
• Own data quality and governance, partnering with algorithm teams to support the data flywheel behind improvements.

Skills

PhD in CS/SE/DS

Distributed systems

SQL

Big data engines

Programming

Education

PhD (completed)

Tools

Spark

Job description

ByteDance in San Jose, CA seeks engineers and researchers for the Applied Machine Learning Ark team to design and operate MaaS platforms across the US and beyond. You will work on end-to-end LLM/AI systems, from data lakes to analytics, shaping model and product decisions.

Successful candidates will contribute to large-scale log analytics, training/fine-tuning/inference frameworks, and intelligent agent systems while collaborating with cross-functional teams. Onboarding by year-end is required.