Research Engineer

About The Role

We’re looking for a Research Engineer to join our AI Training Team and help build the next generation of large-scale AI systems. You’ll work on everything from developing robust training pipelines for LLMs and VLMs to optimizing model performance and integrating state-of-the-art techniques that push our models further — responsibly and efficiently.

What You'll Do:

• Design, implement, and maintain training workflows for large-scale models (LLMs, VLMs, generative models)
• Apply advanced techniques like continual learning, curriculum learning, and reinforcement learning
• Tune hyperparameters, experiment with custom loss functions, and optimize compute resource usage
• Apply post-training techniques (quantization, pruning, knowledge distillation) for efficient deployment
• Evaluate models using standard and task-specific benchmarks
• Conduct error analysis and debug model or data bottlenecks
• Collaborate with data and product teams to align training with deployment needs
• Translate complex model behavior into clear, actionable insights for both technical and non-technical stakeholders

What We're Looking For:

• Ph.D., Master’s, or Bachelor’s degree in Computer Science, Artificial Intelligence, Engineering, or a related technical field. 
• 1–3 years of hands-on experience in training and optimizing machine learning models, with exposure to large-scale development and performance tuning. 
• Strong understanding of deep learning fundamentals and model architectures, including transformers, CNNs, and RNNs. 
• Proficient in Python and experienced with leading ML frameworks such as PyTorch, TensorFlow, or JAX, along with tools for distributed training, experiment tracking, and reproducibility. 
• Working knowledge of the NVIDIA ecosystem including but not limited to CUDA, cuDNN, TensorRT, and multi-GPU training strategies for model development and deployment. 
• Strong problem-solving and collaboration skills, with the ability to work effectively across research, engineering, and infrastructure teams. 
• Bonus: Contributions to research publications, open-source projects, or internal technical tools related to model training or large-scale AI systems.