Machine Learning Software Engineer

About Edgecortix

At Edgecortix we are a deep-tech startup revolutionizing edge computing with artificial intelligence and novel high efficiency silicon on chip design. Originating from multiple years of research, our unique AI hardware & software co-design principle and the Dynamic Neural Accelerator ® AI processor IP are geared towards positively disrupting the rapidly growing artificial intelligence edge hardware space and bring the power of AI and machine learning to all kinds of devices. Our operations are headquartered in Tokyo, Japan, with offices in Singapore, Virginia, and California in the US.

The Team

As an engineering driven company we are working to define and solve the hardest problems in AI including computer vision, speech, and natural language, geared towards real-time capabilities on small to medium form factor devices. We originated out of multiple years of research, as such at our core we value learning, intellectual curiosity, and self-starters. We have the ambitious goal of enabling cloud-level performance with significantly better energy-efficiency for AI inference at the edge.

At EdgeCortix you will:

This role is part of the EdgeCortix’s SAKURA AI Chip team

Work closely with our compiler and hardware engineers to accelerate the adoption of state-of-the-art neural networks on EdgeCortix's proprietary hardware and software stack. Engage in customer-oriented activities guiding deploying of custom and out-of-the-shelf models to meet accuracy and performance targets of customer’s AI system. Grow and maintain a collection of ready-to-use models for an ever-growing set of perception tasks. Work closely with our compiler team to continuously improve stability and user-experience of our software stack.

In this role, you are expected to be an ML expert providing valuable insights and feedback to our architecture team shaping the development of new features. Engage in studies on how various hardware features such as pruning, quantization and mixed-precision impact various aspects of neural network performance across a diverse set of tasks. Stay up to date with common operators and architectural patterns in neural networks from bleeding-edge research to industry-standard models and drive their adoption on EdgeCortix’s stack.

Your day-to-day activities will include but are not limited to dealing with existing neural network implementations, implementing models from scratch, training, quantization, pruning, deployment of models on EdgeCortix hardware, committing accuracy studies, reviewing academic literature, working with public and proprietary vision datasets, constantly communicating with hardware and compiler teams. You will use industry-standard PyTorch and TensorFlow frameworks and do a lot of Python programming.

We’re looking for someone who has:

• Bachelor in Computer Science, Data Science or similar
• Extensive experience in writing production quality Python code
• Experience using TensorFlow and PyTorch ML frameworks
• Strong knowledge of Deep Neural Networks
• Extensive Machine Learning engineering experience. Strong understanding of network implementation, training and accuracy evaluation techniques
• Strong debugging and analysis skills, for root causing complex issues
• Experience with git, and work with github/gitlab development flows

Nice to have:

• Master in Computer Science, Data Science or similar
• Strong object oriented design and development skills
• Expertise in SOTA neural network architectures for popular perception tasks (such as object detection, semantic segmentation, monocular depth estimation), and popular vision datasets (COCO, KTTI)
• Experience in usage of deep learning compiler frameworks such as TVM, Glow or XLA
• Experience in deployment models and development of ML applications on an embedded device
• Extensive experience with network quantization and pruning techniques
• Experience with customizing network implementations and hyper parameter tuning.
• Good knowledge of RNN/LSTMs, and Transformer architectures. Experience with point cloud processing, sequence (such as NLP), and graph processing networks (GNN)
• Experience working in an Agile environment, and collaborating with multidisciplinary teams
• Experience in applying Neural Architecture Search (NAS) techniques is a plus
• Experience in Reinforcement Learning is a plus

Benefits:

• Competitive salary
• Comprehensive paid time off, vacations and sick leaves
• Employee Stock Option Program
• Professional Development Opportunities
• Tons of growth opportunity in a fast paced startup environment