Architect, AI Computing

About Black Sesame Technologies:

Founded in July 2016, Black Sesame Technologies is an AI digital imaging technology firm that creates solutions for real-world AI challenges. The company has developed a radically new chip and system to dramatically accelerate deep learning applications for autonomous.

We are innovating at every level of the stack – from chip, to new algorithms and network architectures at the cutting edge of ML research. Our Core IPs navigate tradeoffs between performance, power, and area, and integrating with flexibility and programmability.

Our software stack is co-designed with the hardware and developed with scalability and quality in mind. Join us to create revolutionary Chips from the ground up.

Job description:

We are seeking a motivated and dedicated hands-on SoC/IP architect to help develop an ASIC for our next-generation artificial intelligence computing architecture alongside a team of world-class scientists and engineers.

In this role, you will be helping us define the architecture for a ground-breaking AI accelerator. Combine the latest innovations in Machine Learning and integrated circuits to create advanced hardware acceleration solutions for Machine Learning training and inference. You will also work closely with our software and ASIC teams to define and optimize the features needed to accelerate the next generation of machine learning algorithms.

Responsibilities:

• Investigate advanced hardware/software architecture to meet future AI computing demand for edge applications and autonomous driving
• Analyze computing demands of application use cases, conducting performance modeling and architecture optimization analysis for BST SoC pertain to core computing sub-systems for vision and AI
• Implement chip functional modeling to help ASIC validation and AI compiler validation. Work closely with ASIC architects, designers, and validation team to ensure the implementation is accurate.
• Design relevant SoC firmware/middleware framework to bring full utilization of the underlined hardware computing capability
• Monitor industrial and academic trends in multimedia and determine where they should intersect our roadmaps

Qualifications:

• Ph. D degree with 3+ years of experience or master’s degree with 5+ years of experience in EE or CS
• Proficient in C/C++ and Python. Experience of embedded programming, firmware design, and CUDA kernel development is a plus.
• Knowledge in basic deep-learning algorithms, i.e. CNN, RNN etc.
• Experience of hardware implementation of deep-learning algorithm
• Understanding of modern SoC architectures and PPA (Performance, Power, and Area) tradeoffs, digital design, and ASIC/SoC micro-architecture