Machine Learning Architect (Software/Hardware Co-Design)

Position Overview:

Black Sesame Technologies is seeking a talented and innovative Machine Learning Research Scientist / Architect with expertise in software/hardware co-design to drive advancements in autonomous driving technologies. This role offers a unique opportunity to bridge the gap between machine learning algorithms and hardware design, contributing to high-performance, efficient, and scalable solutions for AI-driven systems.

Responsibilities:

• Conduct in-depth research and analysis of cutting-edge machine learning algorithms, with a primary focus on applications in autonomous driving.
• Monitor and forecast industry trends in AI technologies to identify opportunities for innovation and improvement.
• Evaluate the computational and memory requirements of various AI models and operators, identifying optimization opportunities.
• Collaborate closely with chip architects and engineers to design and implement software/hardware co-designed solutions that enhance chip performance, efficiency, and scalability.
• Develop comprehensive technical documentation and presentations to effectively communicate complex concepts to both technical and non-technical audiences.
• Stay connected with academic and industry research communities to remain at the forefront of AI and machine learning advancements.

Qualification/ Requirements:

• Education: Master’s or PhD in Computer Science, Electrical Engineering, or a related technical field with a strong focus on machine learning.
• Experience: Proven experience in developing and analyzing machine learning algorithms.
• Programming Skills: Proficiency in programming languages such as Python and C++.
• ML Frameworks: Hands-on experience with machine learning frameworks like TensorFlow and PyTorch, and deploying them on hardware accelerators.
• Collaboration: Demonstrated ability to work effectively in a team-oriented, cross-disciplinary environment.
• Co-Design Expertise: Strong understanding of software/hardware co-design principles and their impact on system performance is highly preferred.