Research Scientist Intern (AI Infrastructure)- 2026 Start (PhD)

Responsibilities About Team: On the AI Infra Team, you'll be immersed in the robust and scalable infrastructure that powers our cutting‑edge artificial intelligence (AI) and machine learning (ML) initiatives. You will work closely with our AI/ML researchers, data scientists, and software engineers to create an efficient, high‑performance environment for training, inference, and data processing. Your expertise will be critical in enabling the next generation of AI‑driven products and services. We are looking for talented individuals to join us for a PhD internship in 2026. PhD internships at ByteDance aim to provide students with the opportunity to actively contribute to our products and research, and to the organization’s future plans and emerging technologies. Infrastructure Design & Architecture Lead end‑to‑end design of scalable, reliable AI infrastructure (AI accelerators, compute clusters, storage, networking) for training and serving large ML workloads. Define and implement service‑oriented, containerized architectures (Kubernetes, VM frameworks, unikernels) optimized for ML performance and security. Performance Optimization Profile and optimize every layer of the ML stack—ML Compiler, GPU/TPU scheduling, NCCL/RDMA networking, data preprocessing, and training/inference frameworks. Develop low‑overhead telemetry and benchmarking frameworks to identify and eliminate bottlenecks in distributed training and serving. Distributed Systems & Scalability Build and operate large‑scale deployment and orchestration systems that auto‑scale across multiple data centers (on‑premises and cloud). Champion fault‑tolerance, high availability, and cost‑efficiency through smart resource management and workload placement. Data Pipeline & Workflow Engineering Architect and implement robust ETL and data ingestion pipelines (Spark/Beam/Dask/Flume) tailored for petabyte‑scale ML datasets. Integrate experiment management and workflow orchestration tools (Airflow, Kubeflow, Metaflow) to streamline research‑to‑production. Collaboration & Mentorship Partner with ML researchers to translate prototype requirements into production‑grade systems. Mentor and coach engineers on best practices in performance tuning, systems design, and reliability engineering. Ideal Candidate The ideal candidate should be an expert in at least one of the following fields to define and design the next‑gen AI Infrastructure: Infrastructure Design & Architecture Lead end‑to‑end design of scalable, reliable AI infrastructure (AI accelerators, compute clusters, storage, networking) for training and serving large ML workloads. Define and implement service‑oriented, containerized architectures (Kubernetes, VM frameworks, unikernels) optimized for ML performance and security. Performance Optimization Profile and optimize every layer of the ML stack—ML Compiler, GPU/TPU scheduling, NCCL/RDMA networking, data preprocessing, and training/inference frameworks. Develop low‑overhead telemetry and benchmarking frameworks to identify and eliminate bottlenecks in distributed training and serving. Distributed Systems & Scalability Build and operate large‑scale deployment and orchestration systems that auto‑scale across multiple data centers (on‑premises and cloud). Champion fault‑tolerance, high availability, and cost‑efficiency through smart resource management and workload placement. Data Pipeline & Workflow Engineering Architect and implement robust ETL and data ingestion pipelines (Spark/Beam/Dask/Flume) tailored for petabyte‑scale ML datasets. Integrate experiment management and workflow orchestration tools (Airflow, Kubeflow, Metaflow) to streamline research‑to‑production. Collaboration & Mentorship Partner with ML researchers to translate prototype requirements into production‑grade systems. Mentor and coach engineers on best practices in performance tuning, systems design, and reliability engineering. Qualifications Graduation date in 2026 year with a PhD in Computer Science, Engineering, or a related technical field. Understanding of infrastructure or systems engineering focused roles, with ML/AI infrastructure. Strong programming skills in Python, C++, Go, or Rust for systems development and automation. Excellent communicator able to bridge research and production teams. Strong problem‑solving aptitude and a drive to push the state of the art in ML infrastructure. Job Information Compensation Description (Hourly) - Campus Intern: The hourly rate range for this position in the selected city is $57-$57. Benefits may vary depending on the nature of employment and the country work location. Interns have day one access to health insurance, life insurance, wellbeing benefits and more. Interns also receive 10 paid holidays per year and paid sick time (56 hours if hired in first half of year, 40 if hired in second half of year). Interns who are not working 100% remote may also be eligible for housing allowance. For Los Angeles County (unincorporated) Candidates Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues; Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; Exercising sound judgment. #J-18808-Ljbffr ByteDance