Principal Machine Learning Research Scientist

- Mid Level

Hybrid - San Diego, CA

Publishing Industries

About the position

At d-Matrix, we are focused on unleashing the potential of generative AI to power the transformation of technology. We are at the forefront of software and hardware innovation, pushing the boundaries of what is possible. Our culture is one of respect and collaboration. We value humility and believe in direct communication. Our team is inclusive, and our differing perspectives allow for better solutions. We are seeking individuals passionate about tackling challenges and are driven by execution. Ready to come find your playground? Together, we can help shape the endless possibilities of AI.

Responsibilities

• Design, implement and evaluate efficient deep neural network architectures and algorithms for d-Matrix's AI compute engine.
• Engage and collaborate with internal and external ML researchers to meet R&D goals.
• Engage and collaborate with SW team to meet stack development milestones.
• Conduct research to guide hardware design.
• Develop and maintain tools for high-level simulation and research.
• Port customer workloads, optimize them for deployment, generate reference implementations and evaluate performance.
• Report and present progress timely and effectively.
• Contribute to publications of papers and intellectual properties.

Requirements

• Master's degree in Computer Science, Electrical and Computer Engineering, or a related technical discipline with 3+ years of industry experience, PhD preferred with 1+ year of industry experience.
• High proficiency with major deep learning frameworks: PyTorch is a must.
• High proficiency in algorithm analysis, data structure, and Python programming is a must.

Nice-to-haves

• Proficiency with C/C++ programming is preferred.
• Proficiency with GPU CUDA programming is preferred.
• Deep, wide and current knowledge in machine learning and modern deep learning is preferred.
• Experience in real-world data science projects in an industry setting is preferred.
• Experience with efficient deep learning is preferred: quantization, sparsity, distillation.
• Experience with specialized HW accelerator systems for deep neural network is preferred.
• Passionate about AI and thriving in a fast-paced and dynamic startup culture.