ByteDance

2 days ago

Senior Research Scientist/Engineer - AI Infrastructure

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Define and build next-generation AI infrastructure across compute, storage, networking, chips, power, and data/application layers for large-scale pretraining, RL, and inference. Translate evolving AI workload requirements into scalable AI-factory architectures, track emerging systems and hardware trends, and optimize ML-stack performance via benchmarking and bottleneck analysis. Collaborate end-to-end across research, engineering, hardware, and product teams to improve reliability and cost efficiency while advancing AI memory and long-horizon agent capabilities.

Location: San Jose

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Design and evaluate scalable AI-factory architectures across compute, storage, networking, chips, power, and data/application layers for training, RL, and inference workloads.
•Develop technical proposals that reconcile supply-chain and energy constraints with silicon and software trade-offs.
•Track emerging trends across distributed training, RL, hardware acceleration, and adjacent cognitive science/psychology research to inform AI memory and reasoning substrates.
•Build prototypes and share insights via technical reports.
•Analyze and optimize end-to-end performance across the ML stack (scheduling, networking, storage, training/RL frameworks, and long-horizon AI memory) using benchmarking and bottleneck analysis, and drive cross-team technical alignment.

Key Requirements

•PhD (or recently completed) in Computer Science, Computer Engineering, Electrical Engineering, or a related technical discipline, with welcome background in cognitive science/computational neuroscience/psychology if paired with strong systems fundamentals.
•Experience in distributed systems, infrastructure engineering, or ML systems, including exposure to large-scale training or RL pipelines, and comfort evaluating trade-offs across hardware, software, algorithms, energy, and supply-chain constraints.
•Strong ability to integrate AI tools into knowledge discovery and research workflows.
•Demonstrated ability to learn quickly and stay productive on a fast-evolving technical horizon.
•Excellent communication skills to collaborate across research, engineering, hardware, and product teams.

Education:PhD / Doctorate

Skills:CommunicationCross-team collaborationLearning agilityBenchmarkingPerformance optimization

Tech Stack:AI infrastructureAI memory systemsDistributed systemsDistributed trainingReinforcement learning (RL)InferencePretrainingKV cacheRetrieval-augmented architecturesAgent long-term memoryGPU/accelerator optimizationHigh-performance networking (RDMA)NCCLHeterogeneous AI computeHPC-style distributed workloadsSchedulingBenchmarkingPrototypingOpen-source

Company Brief

ByteDance

Develops consumer internet and content platforms, including TikTok and other apps for short-form video, news, and entertainment. It also builds advertising, commerce, and creator tools that connect audiences, brands, and publishers across global markets.

Industry: Digital Media

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Established Company

Headquarters: Beijing, China

Founded: 2012

Website LinkedIn

Loading job details...

Preparing the role view and application actions.

ByteDance

2 days ago

Senior Research Scientist/Engineer - AI Infrastructure

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Location: San Jose

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Design and evaluate scalable AI-factory architectures across compute, storage, networking, chips, power, and data/application layers for training, RL, and inference workloads.
•Develop technical proposals that reconcile supply-chain and energy constraints with silicon and software trade-offs.
•Track emerging trends across distributed training, RL, hardware acceleration, and adjacent cognitive science/psychology research to inform AI memory and reasoning substrates.
•Build prototypes and share insights via technical reports.
•Analyze and optimize end-to-end performance across the ML stack (scheduling, networking, storage, training/RL frameworks, and long-horizon AI memory) using benchmarking and bottleneck analysis, and drive cross-team technical alignment.

Key Requirements

•PhD (or recently completed) in Computer Science, Computer Engineering, Electrical Engineering, or a related technical discipline, with welcome background in cognitive science/computational neuroscience/psychology if paired with strong systems fundamentals.
•Experience in distributed systems, infrastructure engineering, or ML systems, including exposure to large-scale training or RL pipelines, and comfort evaluating trade-offs across hardware, software, algorithms, energy, and supply-chain constraints.
•Strong ability to integrate AI tools into knowledge discovery and research workflows.
•Demonstrated ability to learn quickly and stay productive on a fast-evolving technical horizon.
•Excellent communication skills to collaborate across research, engineering, hardware, and product teams.

Education:PhD / Doctorate

Skills:CommunicationCross-team collaborationLearning agilityBenchmarkingPerformance optimization

Company Brief

ByteDance

Industry: Digital Media

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Established Company

Headquarters: Beijing, China

Founded: 2012

Website LinkedIn