Research Engineer Intern

Job Description:

• 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.

Qualification:

• 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.

Nice to Have:

• 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.