Foundation AI Engineer (LLM)

VINFAST is a pioneering electric vehicle (EV) company committed to revolutionizing the automotive industry with sustainable and innovative mobility solutions. As a leading player in the EV market, VinFast is dedicated to delivering high-quality, cutting-edge electric vehicles that redefine the driving experience. Our team consists of passionate professionals driven by a shared vision of creating a greener and more sustainable future through innovation, technology, and excellence.

In this role, you will be instrumental in SLP Center, using your skills to process and analyze large volumes of data. You will collaborate with diverse teams, including Engineering, Quality Control, VINFAST's suppliers, to create cutting-edge solutions that will drive the future of transportation.

• Research, design, and train foundation language models (LLM/SLM) powering robot and virtual assistant products, spanning the full lifecycle from pretraining through post-training.
  

• Design and implement pretraining pipelines: large-scale data preparation and cleaning, tokenization, model architecture design (dense, MoE), scaling strategy selection (data/model/compute scaling laws), and distributed training across large GPU clusters.
  

• Conduct continued pretraining / domain-adaptive pretraining to specialize models for Vietnamese-language data, dialogue, or the robot/virtual assistant domain.
  

• Design and implement post-training techniques: Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), Reinforcement Learning from AI Feedback (RLAIF), Direct Preference Optimization (DPO), PPO, GRPO, and other preference optimization variants.
  

• Build data pipelines for preference/instruction data collection and processing (data curation, synthetic data generation, data mixing, deduplication, quality filtering).
  

• Research and apply alignment techniques: reward modeling, constitutional AI, safety tuning, and methods for reducing hallucination.
  

• Optimize training performance: distributed training (data/tensor/pipeline/expert parallelism), mixed precision, gradient checkpointing, and other memory/compute-saving techniques.
  

• Design and implement model compression and distillation methods to produce efficient SLMs for on-device/edge deployment (robots, virtual assistants).
  

• Build evaluation frameworks across training stages: internal benchmarks, automatic evaluation, human evaluation, and safety/alignment test suites.
  

• Track the latest research in pretraining/post-training (papers, open-weight models, training techniques) and rapidly prototype and integrate promising methods into production pipelines.
  

• Collaborate with Agentic AI, Data, and Infra teams to ensure foundation models meet production deployment requirements (latency, cost, scalability).
  

• Mentor junior engineers, conduct technical reviews, and contribute to engineering standards for the Foundation Model team.
  

• 5+ years of experience training/fine-tuning large language models, or deep research experience in deep learning/NLP. (Adjust based on seniority.)
  

• Strong understanding of Transformer architecture, attention mechanisms, positional encoding, and modern architectural variants (MoE, SSM, hybrid architectures).
  

• Hands-on experience with LLM pretraining from scratch or continued pretraining, including data pipeline design, tokenizer training, and scaling.
  

• Proficiency in post-training techniques: SFT, RLHF/RLAIF, DPO, PPO, with clear understanding of the tradeoffs between methods (offline vs. online RL, reward-based vs. reference-free).
  

• Experience building reward models and preference data collection pipelines (human or AI feedback).
  

• Strong Python skills and experience with distributed training frameworks: PyTorch, DeepSpeed, FSDP, Megatron-LM, or equivalent.
  

• Experience with training/fine-tuning libraries: Hugging Face Transformers, TRL, PEFT, Axolotl, or equivalent.
  

• Understanding and hands-on experience with parameter-efficient training techniques (LoRA, QLoRA) as well as large-scale full fine-tuning.
  

• Experience working with multi-node/multi-GPU infrastructure, optimizing communication overhead and memory usage.
  

• Strong analytical skills, ability to read research papers and rapidly translate them into experimental pipelines.
  

• Strong communication skills and ability to work cross-functionally with other teams.
  
  

• Experience training or fine-tuning models for Vietnamese or other low-resource languages.
  
• Experience with inference optimization and serving (vLLM, SGLang, TensorRT-LLM) for in-the-loop model evaluation during training.
  

• Experience with model distillation, quantization-aware training, or other model compression techniques for edge/on-device deployment.
  

• Publications at tier-1 venues (NeurIPS, ICML, ICLR, ACL, EMNLP) related to pretraining, alignment, or RL for LLMs.
  

• Contributions to open-source LLM training projects (e.g., open pretraining recipes, RLHF frameworks).
  

• Experience with Kubernetes, GPU cluster infrastructure, and MLOps platforms for large-scale training pipelines.