Job Description:

Deep Learning Systems ArchitectMake your application after reading the following skill and qualification requirements for this position.Are you prepared to spearhead the evolution of AI, ensuring its seamless integration into everyday life? At our company, we're committed to democratizing AI, making it accessible, efficient, and transformative for all. Our vision is to empower developers and enterprises globally by revolutionizing on-device inference and advancing the frontiers of AI technology.About UsAt our company, we're at the forefront of AI innovation. Our dedicated team is currently at work developing state-of-the-art technologies, including a versatile Inference Engine compatible with diverse platforms, Swift packages for end-to-end inference pipelines, a Python toolkit for optimizing model performance, and fostering an inclusive developer community.Why Join Us?As a Deep Learning Systems Architect at our company, you'll enjoy:Close collaboration with industry experts, partners, and collaborators. Opportunities to contribute to open-source projects and share insights through technical publications. Autonomy to drive projects in alignment with business objectives. Advocacy for increased R&D investments supported by empirical data.What We OfferCompetitive equity-based compensation reflecting market standards. Flexibility to work from vibrant locations like Los Angeles, San Francisco, or New York City. Comprehensive health insurance and 401(k) plans with employer matching. Opportunities for travel to attend team gatherings and industry conferences.Key ResponsibilitiesAs a Deep Learning Systems Architect, your duties will include:Profiling and optimizing the performance of deep learning workloads across diverse platforms, such as Nvidia, Apple, and Qualcomm. Designing and implementing highly efficient GPU kernels for our Inference Engine. Communicating complex technical concepts through accessible technical blogs tailored to our audience. Providing mentorship to junior team members and interns.QualificationsTo thrive in this role, you should possess:Expertise in debugging, profiling, and optimizing GPU kernels. Proficiency in parallel programming techniques. Familiarity with Metal and/or CUDA/Triton frameworks. A deep understanding of the characteristics of modern deep learning workloads. A foundational knowledge of key machine learning concepts. Experience with Android/Windows platforms is considered advantageous.