Research Scientist - AI
Job Description
NVIDIA is searching for outstanding AI researchers to join the NVIDIA Research Singapore team. We are dedicated to the development and optimization of GPU-accelerated efficient AI computing, covering language model, visual generation, and robotics. We focuses on pushing the boundaries of generative AI by designing models that are not only powerful but also efficient in terms of computational resources.
You will be part of an amazing collaborative research team that consistently publishes at the top venues in machine learning, robotics and computer vision. Your contributions have the chance to create real impact on our products.
What You'll Be Doing
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You will be part of an amazing collaborative research team that consistently publishes at the top venues in machine learning, robotics and computer vision. Your contributions have the chance to create real impact on our products.
What You'll Be Doing
- Research, design and accelerate novel generative AI models
- Publish original research
- Collaborate with other team members and teams
- Mentor interns
- Speak at conferences and events
- Transfer technology to product groups
- Collaborate with external researchers
- Excellent knowledge of LLM and foundation models
- Publication at leading conferences (ie. CVPR, ICML, ICLR, ICCV, NeurIPS, etc)
- 3+ years of experience.
- Bachelor, master, or PhD degree in computer science or electrical engineering with strong research track records.
- Excellent programming skills in some rapid prototyping environment such as Python; C++ and parallel programming (e.g., CUDA) is a plus
- Knowledge of common machine learning frameworks, such as PyTorch
- Excellent communication skills
, , JR2020802
More Info
About Company
Skills:
Machine Learning, Unsupervised Learning, Python, agentic AI, adversarial machine learning, debugging complex ML failure modes, reverse engineering model behaviors, supervised learning, generative models, reinforcement learning, data poisoning, ML safety, evaluating frontier AI systems, prompt injection, ML interpretability, multi-object optimization, adversarial robustness, model backdoor detection, adversarial machine learning AI agent security, red-teaming frontier AI systems
Skills:
Pytorch, Python, Cuda, LLM and foundation models
Skills:
causal inference , Jax, Pytorch, Neural ODEs, Bayesian deep learning, Foundation model fine-tuning, Graph neural networks, structural causal models, PINNs methodology, Bayesian experimental design, RL for scientific discovery, causal discovery, active learning acquisition function design, diffusion models
Skills:
alpha generation , Jax, Pytorch, Execution, pre-training large foundation models, transfer across instruments, self-supervised objectives, evaluation protocols, model-serving components, market making, data efficiency, large-scale market datasets, time-series foundation models, tokenization schemes, model architectures, data representations, distributed training recipes, risk-management tasks, scaling laws, regime robustness, training checkpointing, Pricing
Skills:
Machine Learning, Cnn, Deep Learning, Tensorflow, Rnn, Pytorch, Python, Transformer, LSTM, ViT, MxNet, Caffe, Model Compression