AI Research Scientist

What you'll do

• Develop and optimize models, architectures, training methods, and inference strategies for distributed AI.
• Research model efficiency and on-device deployment; build benchmarks, datasets, and experiments to evaluate performance.
• Collaborate with engineering teams to integrate research into production and communicate and document findings.

What they ask for

• 4+ years of experience (can be graduate research) in machine learning research, AI model development, or related fields.
• Expertise in deep learning architectures, including transformers, CNNs, RNNs, and diffusion models; hands-on experience training and fine-tuning large-scale models.
• Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or JAX; experience building datasets, designing experiments, and validating model performance.
• Deep understanding of optimization techniques including quantization, distillation, pruning, and hardware-aware training; bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field.

Nice to have

• Master’s or PhD in Machine Learning, Computer Science, AI, or a related field.
• Experience in distributed training, edge inference, on-device ML, generative AI, reinforcement learning, multimodal learning, federated learning, publications, patents, or open-source ML projects.

Tools

Python, PyTorch, TensorFlow, JAX, LLMs, transformers, CNNs, RNNs

AI focus

Research and development of LLMs, multimodal and diffusion models; model efficiency, quantization, compression, and on-device inference for distributed, privacy-preserving AI.