Software Engineer, AI Framework

About Black Sesame Technologies

Founded in July 2016, Black Sesame Technologies is a leading supplier of automotive-grade intelligent vehicle computing chips and chip-based solutions. Our team has pioneered new chip designs and systems to accelerate deep learning applications for AD and ADAS. We innovate across the entire stack, from chip design to cutting-edge machine learning algorithms, creating revolutionary solutions that balance performance, power, and flexibility.

Our focus on building a co-designed hardware-software ecosystem ensures scalability and quality at every level. Join us as we revolutionize autonomous driving technologies from the ground up.

Job description:

We are looking for an experienced AI/ML engineer passionate about deploying AI inference and end to end enablement, AI framework integration, model accuracy, and performance analysis and tuning. You might be an ideal candidate if you are seeking to develop high quality, innovative, and scalable software that enables state of the art AI inference models to run efficiently and accurately on the BST Intelligence processors.

Responsibilities:

• Contributing to deep learning infrastructure, data pipelines, tools and workflows that lay the foundation for building AI at scale.
• Writing software to deploy AI models and pipelines in real time applications (inference).
• Apply low precision inference, quantization, and compression of DNNs.
• Continuously improve inference latency, accuracy and power consumption of DNNs.
• Stay up to date with the latest research and innovations in deep learning, implement and experiment with new ideas

Qualifications:

• You are graduating with a MS, or Ph.D. degree in Computer Science, Computer Engineering, Applied Math, or related field.
• You can work independently, define project goals and scope, and lead your own development effort.
• Strong Python or C++/C programming and software design skills, including debugging, performance analysis, and test design.
• Experience with Deep Learning Frameworks such as PyTorch, TensorFlow
• Experience with model acceleration techniques such as deep learning quantization, model pruning, and model distillation.

Ways to stand out from the crowd:

• Experience with numerical methods
• Knowledge of computer architecture
• Experience with AI compilers
• Experience in model deployment within the autonomous driving industry
• Experience deploying LLMs on edge devices