<p>We are looking for talented individuals to join our team in 2026. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at ByteDance.</p> <p>Successful candidates must be able to commit to an onboarding date by end of year 2026. Please state your availability and graduation date clearly in your resume.</p> <p>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.</p> <p>Responsibilities The ideal candidate should be an expert in at least one of the following fields to define and design the next-gen AI Infrastructure:</p> <ul> <li>Infrastructure Design & Architecture </li><li>Lead end-to-end design of scalable, reliable AI infrastructure (AI accelerators, compute clusters, storage, networking) for training and serving large ML workloads. </li><li>Define and implement service-oriented, containerized architectures (Kubernetes, VM frameworks, unikernels) optimized for ML performance and security. </li><li>Performance Optimization </li><li>Profile and optimize every layer of the ML stack-ML Compiler, GPU/TPU scheduling, NCCL/RDMA networking, data preprocessing, and training/inference frameworks. </li><li>Develop low-overhead telemetry and benchmarking frameworks to identify and eliminate bottlenecks in distributed training and serving. </li><li>Distributed Systems & Scalability </li><li>Build and operate large-scale deployment and orchestration systems that auto-scale across multiple data centers (on-premises and cloud). </li><li>Champion fault-tolerance, high availability, and cost-efficiency through smart resource management and workload placement. </li><li>Data Pipeline & Workflow Engineering </li><li>Architect and implement robust ETL and data ingestion pipelines (Spark/Beam/Dask/Flume) tailored for petabyte-scale ML datasets. </li><li>Integrate experiment management and workflow orchestration tools (Airflow, Kubeflow, Metaflow) to streamline research-to-production. </li><li>Collaboration & Mentorship </li><li>Partner with ML researchers to translate prototype requirements into production-grade systems. </li><li>Mentor and coach engineers on best practices in performance tuning, systems design, and reliability engineering. </li></ul>