{"id":3361961,"url":"https://alion.io/job/bytedance-research-engineer-seed-model-infra-pretraining-infrastructure","title":"Research Engineer - (Seed Model - Infra - Pretraining Infrastructure)","company":{"id":13,"name":"ByteDance","domain":"bytedance.com","url":"https://alion.io/company/bytedance","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Feishu","truth_index":{"grade":"A","score":89,"open_postings":9920,"ghost_share":0.107,"stale_share":0,"repost_share":0.003,"time_to_fill_p50_days":314,"computed_at":"2026-10-30T05:48:23Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Jose, United 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Responsibilities\nAbout the Team\nThe Seed Infrastructures team oversees the distributed training, reinforcement learning framework, high-performance inference, and heterogeneous hardware compilation technologies for AI foundation models.\nResponsibilities\n- Conduct research and engineering development on large-scale infrastructure to enable efficient pretraining of foundation models, multimodal LLMs, and video generation models\n- Design and optimize distributed training strategies for model training, including parallelism schemes, computation and communication optimization, and throughput scaling on large GPU clusters\n- Analyze performance bottlenecks in exascale training systems and propose principled, data-driven optimization methods\n- Bridge cutting-edge research and large-scale production deployment by translating research ideas into scalable, real-world infrastructure solutions\n任职要求 / Requirements\nMinimum Qualifications\n- Bachelor's degree or above in Computer Science, Software Engineering or a related field, with 2+ years of experience in large-scale model training, distributed training systems, or GPU kernel optimization\n- Solid understanding of large-scale training of LLMs and multimodal models\n- Proven experience with parallelism strategies (e.g., data, pipeline, expert parallelism) on large GPU clusters, or GPU kernel performance optimization\n- Strong grasp of algorithm-system co-design and cross-layer optimization for training efficiency, scalability, and reliability\n- Strong programming skills and hands-on experience implementing production-grade ML systems or infrastructure\nPreferred Qualifications\n- Hands-on experience pretraining LLMs, multimodal, or video generation models at scale, or with mainstream distributed training frameworks (e.g., Megatron-LM, DeepSpeed, PyTorch FSDP, NCCL), or with custom kernel development using CUDA or Triton\n- Publications at top-tier machine learning or systems conferences (e.g., NeurIPS, ICML, MLSys, 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