AI Researcher

Responsibilities

• Design and evaluate training optimization techniques for large models including optimization algorithms, schedulers, and normalization
• Improve training efficiency and stability across long runs and large datasets
• Research and implement methods such as mixed-precision, low-precision, and memory-efficient training
• Conduct gradient noise reduction, scaling laws, and convergence analysis
• Run large-scale experiments, analyze results, and translate findings into actionable improvements
• Author or co-author research papers, technical reports, or blog posts
• Collaborate with infrastructure and inference teams to ensure training decisions translate to real-world performance

Requirements

• Strong background in machine learning research with emphasis on training dynamics and optimization
• Experience training large neural networks such as LLMs, multimodal models, or large sequence models
• Publication experience in ML venues like NeurIPS, ICML, ICLR, ACL, EMNLP, COLM, or arXiv
• Solid understanding of optimization theory, backpropagation, gradient flow, and training stability
• Knowledge of distributed and large-batch training
• Proficiency in Python and modern ML frameworks, preferably PyTorch
• Ability to independently design experiments and reason from data

Preferred Qualifications

• Experience with non-standard architectures like RNN variants, long-context models, or hybrid systems
• Experience optimizing training on GPUs at scale using FSDP, ZeRO, or custom kernels
• Contributions to open-source ML or research codebases
• Comfort operating in fast-moving, ambiguous startup environments