Engineer, AI Computing

Company Overview Black Sesame Technologies is a leading automotive-grade computing SoC and intelligent vehicle solution provider. Founded in 2016 and listed on the Hong Kong Stock Exchange, it drives innovation in autonomous driving, smart cockpit, imaging, embodied AI and connectivity technologies. Job Summary Black Sesame Technologies seeks an Engineer, AI Computing to advance autonomous driving by bridging machine learning algorithms with hardware design, delivering high-performance, efficient AI-driven system solutions. Responsibilities • Conduct in-depth research and analysis of machine learning algorithms focused on autonomous driving applications to drive innovation. • Evaluate computational and memory requirements of AI models and operators to identify and implement optimization opportunities. • Collaborate with chip architects and engineers to design and implement software/hardware co-designed solutions that improve chip performance, efficiency, and scalability. • Develop clear technical documentation and presentations to communicate complex AI and hardware concepts to diverse audiences. • Monitor and analyze AI industry trends to identify innovation opportunities and guide strategic development. • Engage actively with academic and industry research communities to stay updated on AI and machine learning advancements. Required competencies and certifications • Master’s or PhD degree in Computer Science, Electrical Engineering, or a related technical field with a strong focus on machine learning. • Minimum 2 years of experience developing and analyzing machine learning algorithms. • Proficiency in programming languages including Python and C++. • Hands-on experience with machine learning frameworks such as TensorFlow and PyTorch, including deployment on hardware accelerators. • Demonstrated ability to collaborate effectively in cross-disciplinary, team-oriented environments. Preferred competencies and qualifications • Strong understanding of software/hardware co-design principles and their impact on system performance.