Research Engineer Intern at GenBio AI in Palo Alto, CA

Summary: GenBio AI is a newly established start-up headquartered in Silicon Valley, dedicated to transforming biology and medicine through generative AI. The role involves conducting research in AI and computational biology, collaborating with a team to improve models and analyze datasets to advance AI capabilities in biology. Responsibilities: - You will work with the team to conduct cutting-edge research in AI, foundation models, and computational biology - Your primary tasks will include improving existing models and exploring new methodologies to advance our AI capabilities in biology - You will collaborate with the team on designing and executing large-scale experiments, analyzing complex datasets, and applying statistical techniques to validate the performance and robustness of AI systems - Additionally, you will work closely with AI/machine learning researchers and computational biologists to develop Genbio AI’s state-of-the-art biology foundation models and drive the research agenda to generate impact Required Qualifications: - Currently enrolled in a full-time master's or PhD (preferred) program in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field (preferably in the United States) - A strong coder with excellent skills in C/C++ and Python - Fluent in deep learning frameworks like PyTorch (and/or JAX), Hugging Face (Datasets, Accelerate, Transformers, etc.), Megatron-LM, DeepSpeed, etc - Have a solid understanding of GPU, CPU, or other AI accelerator architectures - Familiar with LLM (and/or other foundation model) architectures (such as attention mechanisms, state-space models, MoE, etc.) and training infrastructure (e.g., large-scale GPU clusters) - Have experience improving ML accuracy using low-precision formats - Have 1+ years of relevant industry experience - Derive a great deal of satisfaction from every percentage point of performance improvement - Have experience writing and optimizing compute kernels using CUDA or similar languages Preferred Qualifications: - Current PhD in Computer Science and Engineering with a specialization in Computer Architecture, Parallel Computing, Compilers, or other systems - Co-optimizing computing infrastructure and deep learning frameworks for optimal performance on specific workloads. Identify and resolve performance bottlenecks through profiling and system analysis - Experience collaborating with data scientists and machine learning engineers to integrate distributed training capabilities into GenBio AI's model development and deployment frameworks - Proficient in Python with experience in GPU-accelerated libraries (e.g., CUDA, cuDNN) - Knowledge of performance profiling and optimization tools for HPC and deep learning Required Skills: C/C++, Python, Deep learning frameworks, GPU architectures Important Skills: LLM architectures, CUDA, Machine Learning Nice-to-Have Skills: Performance optimization