Machine Learning Research Intern

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. 

Location

Hybrid, working onsite at our Santa Clara, CA headquarters 3 days per week.

June 2nd - August 15th

Machine Learning Research Intern

The Machine Learning Team is responsible for the R&D of core algorithm-hardware co-design capabilities in d-Matrix's end-to-end solution.  You will be joining a team of exceptional people enthusiastic about researching and developing state-of-the-art efficient deep learning techniques tailored for d-Matrix's AI compute engine.  You will also have the opportunity of collaboration with top academic labs and help customers to optimize and deploy workloads for real-world AI applications on our systems. 

What You Will Do:

• 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. 

• Work on making the latest generative AI more efficient - LLM, Diffusion Models

What You Will Bring:

• Pursuing Bachelor/Master/PhD degree in Computer Science, Electrical and Computer Engineering, or a related scientific discipline. 

• High proficiency with major deep learning frameworks: PyTorch, TensorFlow is a must. 

• High proficiency in algorithm analysis, data structure, and Python programming is a must. 

• Deep, wide and current knowledge in machine learning and modern deep learning. 

• Hands-on experience with CNN, RNN, Transformer neural network architectures. 

• Knowledge and experience with efficient deep learning is preferred: quantization, sparsity, distillation.

• Strong publication records in top machine learning conferences or journals. 

• Proficiency with C/C++ programming is preferred. 

• Proficiency with GPU CUDA programming is preferred.

• Experience with AutoML and meta learning is preferred. 

• Experience with numerical analysis preferred. 

• 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.

Equal Opportunity Employment Policy

d-Matrix is proud to be an equal opportunity workplace and affirmative action employer. We’re committed to fostering an inclusive environment where everyone feels welcomed and empowered to do their best work. We hire the best talent for our teams, regardless of race, religion, color, age, disability, sex, gender identity, sexual orientation, ancestry, genetic information, marital status, national origin, political affiliation, or veteran status. Our focus is on hiring teammates with humble expertise, kindness, dedication and a willingness to embrace challenges and learn together every day.

d-Matrix does not accept resumes or candidate submissions from external agencies. We appreciate the interest and effort of recruitment firms, but we kindly request that individual interested in opportunities with d-Matrix apply directly through our official channels. This approach allows us to streamline our hiring processes and maintain a consistent and fair evaluation of al applicants. Thank you for your understanding and cooperation.