AI Researcher - Content Video Generation (Pre-training)

What matters most

• Build large-scale training toolchains

  The role designs distributed training toolchains for ultra-large-scale models. Prepare to explain how data, pipeline, tensor and expert parallelism fit into a coherent training design.

• Improve end-to-end training performance

  The posting calls for optimization across computation, communication and storage. Prepare a concrete approach to measuring bottlenecks and deciding which layer to address first.

• Increase stability and scalability

  Resolving training bottlenecks to improve stability, scalability and efficiency is an explicit duty. Prepare to discuss how to diagnose failures and verify that a fix holds at larger scale.

• Turn research into production-grade systems

  The role tracks emerging distributed-training technologies and leads planning and production-grade implementation. Ground preparation in the stated context of video pre-training and high-fidelity portrait and video generation, rather than unrelated AI applications.

Likely challenges to think through

• Choosing a parallelism strategy

  Preparation hypothesis: A strategy that eases model-memory pressure may add communication or pipeline overhead. Practice explaining how workload characteristics and measurements would guide a choice among the parallelism methods named in the posting.

• Isolating cross-layer slowdowns

  Preparation hypothesis: Poor throughput may stem from GPU computation, collective communication or storage, making a single-layer fix misleading. Prepare a measurement-led diagnostic sequence and criteria for confirming the actual bottleneck.

• Balancing novelty with reliability

  Preparation hypothesis: An emerging training technique may promise efficiency but introduce stability or implementation risk. Prepare to explain how to evaluate it against a baseline before committing to production-grade implementation.

Topics to prepare

• Distributed-training parallelism

  The posting explicitly requires deep understanding of data, pipeline, tensor and expert parallelism. Suggested revision exercise: sketch how each partitions a large model or workload, then explain its communication costs and when you would combine approaches.

• Training-system bottlenecks

  The role calls for optimizing computation, communication and storage while improving stability and efficiency. Suggested revision exercise: reason through a training run that slows as it scales; identify a plausible bottleneck in each layer and how you would test your diagnosis.

• GPU and library fundamentals

  The requirements name GPU architecture, CUDA, NCCL and cuDNN. Suggested revision exercise: trace a training step from GPU computation through inter-GPU communication, explaining where each named technology fits and what could limit throughput.

• Video-generation model foundations

  The posting requires understanding of video pre-training, Transformer architectures and diffusion models, citing Stable Diffusion and Flux as examples. Suggested revision exercise: explain the training objective and scaling considerations for a video-generation approach, distinguishing model concepts from training infrastructure.

• Stability and production-grade scaling

  The job includes resolving training bottlenecks and leading production-grade implementation. Suggested revision exercise: outline how you would distinguish a numerical-stability problem from a distributed-systems failure and decide whether a proposed optimization is ready to deploy.

Skills to revise

• Build a distributed training toolchain

  Toolchain development is a stated responsibility, and the posting names PyTorch, DeepSpeed and Megatron-LM as framework examples. Suggested practice: design a small training workflow using one of those frameworks and explain its launch, checkpoint and failure-recovery choices; these choices are practice prompts, not specified employer requirements.

• Configure and compare parallel training

  Hands-on distributed training across data, pipeline, tensor and expert parallelism is required. Suggested practice: configure two parallelism strategies for a representative model and compare their memory use, communication and throughput, noting what changes as the run scales.

• Diagnose training performance

  The role explicitly calls for finding bottlenecks across computation, communication and storage. Suggested practice: use measurements from a sample training run to locate its dominant cost, propose one change and define how you would verify an improvement without sacrificing stability.

• Develop and debug CUDA kernels

  CUDA kernel development and debugging are named requirements. Suggested practice: implement a small training-related CUDA operation, check its output against a reference implementation and investigate a correctness or performance issue.

• Plan and communicate an infrastructure change

  The posting calls for project planning, production-grade implementation, collaboration and communication. Suggested practice: present a short proposal for a distributed-training improvement, including the bottleneck evidence, expected trade-offs, validation plan and questions for collaborators.

• Map evidence to the requirements

  Prepare a one-page matrix of relevant experience, education, and examples for the stated distributed-training, framework, GPU, and video-model requirements; mark any gaps rather than implying experience you do not have.

• Rehearse a distributed-training project walkthrough

  Choose a project you can discuss and outline your contribution, model scale, data/pipeline/tensor/expert parallelism choices, framework use, measured results, and trade-offs. The role asks for hands-on distributed-training experience.

• Sketch an ultra-large-scale training toolchain

  As interview practice, draw a proposed toolchain for video-model pre-training and explain how you would coordinate workers, checkpoint runs, and test changes. These are design prompts, not a stated Shopee architecture.

• Prepare a bottleneck investigation

  Bring a concrete example or practice scenario showing how you would measure computation, communication, and storage costs, diagnose instability, and verify a scalability or efficiency improvement.

• Review the GPU and framework details

  Prepare to explain relevant PyTorch, DeepSpeed, or Megatron-LM work and, where applicable, CUDA kernel development or debugging, GPU architecture, NCCL, and cuDNN. Distinguish direct experience from conceptual knowledge.

• Connect model methods to video pre-training

  Make a short comparison of Transformer-based and diffusion-based approaches, including Stable Diffusion or Flux where relevant, and explain how their training demands could affect distributed-system choices.

• Outline a research-to-implementation plan

  Choose an emerging distributed-training technique to discuss. Draft how you would evaluate it, plan a project, collaborate on decisions, and validate a production-grade implementation; the role explicitly combines frontier research with implementation.

• Write targeted questions for the team

  Ask which portrait and video pre-training bottlenecks matter most, how the team evaluates training stability and efficiency, and where toolchain research meets production implementation.