Backend Software Engineer, Model Training
4 weeks ago
Why JoinUs
Creation is the core of TikTok's purpose. Ourplatform is built to help imaginations thrive. This is doubly trueof the teams that make TikTok possible. Together, we inspirecreativity and bring joy - a mission we all believe in and aimtowards achieving every day. To us, every challenge, no matter howdifficult, is an opportunity; to learn, to innovate, and to grow asone team. Status quo? Never. Courage? Always. At TikTok, we createtogether and grow together. That's how we drive impact - forourselves, our company, and the communities we serve. Joinus.
The Trust and Safety(TnS) engineering teamis responsible for protecting our users from harmful content andabusive behaviors. With the continuous efforts of our trust andsafety engineering team, TikTok can provide the best userexperience and bring joy to everyone in the world. Our team isresponsible for achieving goals by building content moderationprocess systems, rule engine, strategy systems, feature engine,human moderation platforms, risk insight systems and all kinds ofsupportive platforms across TnSorganization.
Responsibilities - What You'II Do
1. Work closely with business teams to optimizethe integration plan for algorithm applications, improve efficiencyin evaluating and using algorithm applications across variousbusiness scenarios, and reduce the cost of managing and optimizingalgorithm applications in different business scenarios.
2. Be responsible for the architectural design,development, and performance tuning of algorithm applications,solving technical challenges such as high concurrency, highreliability, and high scalability. Work includes multiplesub-areas: ML model training and evaluation, model optimization,model inference, model management, dataset management, workfloworchestration, etc.
3. Responsible for thedesign and development of Machine Learning infrastructure forLLM/AIGC, etc
4. Build up a super large machinelearning system integrating GPUs, RDMA networking, andhigh-performance storage
5. Be responsible forresearching and implementing cutting-edge engineering technologiesrelated to LLM, NLP,CV.
Qualifications
MinimumQualification
- At least 2 years of industrialexperience;
- Hands-on experience in one ormore of the following areas: Machine Learning, Deep Learning,Recommender Systems, Natural Language Processing, or ComputerVision
- Be proficient in 1 to 2 programminglanguages such as C++/Go/Python/Shell in Linux environmentUnderstand the principles of distributed systems and haveexperience in design, development and maintenance of large-scalemachine learning systems
- Be familiar withKubernetes architecture, and have rich experience in system-leveldevelopment and tuning Familiar with the ML Infrastructure of LargeModel training and inference
- Strongunderstanding and engineering experience of cutting-edge LLMresearch and engineering (e.g., long context, multi modality,active learning, alignment research, agent ecosystem, etc.) andpossess practical expertise in effectively implementing theseadvanced systems.
- Proficiency in programminglanguages such as Python, CUDA or C++ and a track record of workingwith deep learning frameworks (e.g., pytorch, deepspeed, megatron,vllm, etc.).
- Have experience with large scaledata processing and parallel computing
PreferredQualifications
- Excellent programming skills,data structure and algorithm skills, proficient in C/C++ or Pythonprogramming language, candidates with awards in ACM/ICPC, NOI/IOI,Top Coder, Kaggle and other competitions are preferred.
- Research or industry experience in the fieldof machine learning, especially in large language models (LLMs) andgenerative artificial intelligence.
-Distributed training framework optimizations such as DeepSpeed,FSDP, Megatron, GSPMD Experiences in in-depth CUDA programming andperformance tuning (cutlass, triton) Experience with evaluation ofML models, LLM application & agent development is desirable.
- PhD/Master's degree required, with topartificial intelligence conference papers (NeurIPS, ICML, ICLR,CVPR, ACL, EMNLP, etc.) in machine learning (ML), computer vision(CV), natural language processing (NLP) and other fields.
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