Deep Learning Engineer

NextSilicon is reimagining high-performance computing. Our accelerated compute solutions leverage intelligent adaptive algorithms to vastly accelerate supercomputers, driving them forward into a new generation. Our new software-defined hardware architecture enables HPC & AI to fulfill their promise of breakthroughs in all fields of advanced research. 

At NextSilicon, everything we do is guided by three core values:

• Professionalism: We get exceptional results through professionalism and unwavering dedication to quality and performance.
• Unity: Collaboration is key to success; we foster a work environment where every employee feels valued and heard.
• Impact: We're passionate about developing technologies that make a meaningful impact on industries, communities, and individuals worldwide.

The AI Application team plays a major role in accelerating cutting-edge AI models and workflows, with world-record results, on NextSilicon unique hardware architecture.

We are seeking a talented Deep Learning Engineer with extensive experience in running, profiling and optimizing AI models and end-to-end workflows. This position sits at the intersection of computer science, hardware architecture and ML computational algorithms, requiring technical expertise, theoretical knowledge, and creative problem-solving.

• B.Sc. degree in software engineering, computer science, or a related field; advanced degrees are a plus (M.Sc. or Ph.D.)
• Proficiency in Python and C/C++ 
• 4+ years of experience in software engineering
• 2+ years hands-on experience with profiling, analyzing, and optimizing AI models.
• Excellent command of PyTorch or TensorFlow 
• Expertise in competitive performance analysis is strongly preferred
• At least one of the following:
• Demonstrable open-source contribution to relevant projects.
• Experience developing state-of-the-art neural network architectures.
• Specialization experience in ML accelerators, high performance computing, GPU and/or CPU architectures.

• Profiling and analyzing AI models in hardware and software
• Identifying bottlenecks and optimizing model performance on NextSilicon novel architectures.
• Deriving next-gen software and requirements and specifications.
• Collaborating with software and hardware architects to define and achieve numerical requirements.