Senior Software Engineer - Machine Learning

Developing a software pipeline for end-to-end ML Model Inference for specific hardware accelerator by achieving maximum performance & accuracy. 

• Implementing cutting edge deep learning layers for various model categories like CNN, RNN, LSTM, GANs, etc using customized inference pipeline for NN Processor. 
  
• Performance optimization for inferencing the LLM Models in customized hardware with various layer types including transformer, encoder-decoder, etc based models.
  
• Hardware architecture aware and computation conscious implementation of solutions in an embedded device and maximize the throughput. 
  
• Develop tools and applications by producing clean, efficient code. 
  
• Identify, prioritize and execute tasks based on requirement. 
  
• Implementation, Review, Debug code, Product Delivery and quick turn arounds. 
  
• Collaborate with team to brainstorm and create new products. 
  
• Mentor fresh joiners and foster team culture. 
  

Must-Have:

• BE/BTech/MS/MTech graduates with Computer science Engineering with 4+ yrs of experience. 
  
• Solid programming experience in C/C++ with proven experience as Senior Software Engineer. 
  
• Experience in implementing kernel intrinsics for Machine Learning or Computer Vision algorithms with a focus on optimization. 
  
• Extensive experience in software development and project management. 
  
• Strong analytical and problem-solving skills. 
  
• Adaptable to execute complex tasks, under tight schedules and dynamic conditions. 
  
• Familiarity with various operating systems (Linux, Mac OS, Windows). 
  
• Ability to work independently and manage a team. 
  
• Excellent organizational and leadership skills. 
  
• Working knowledge on Deep Learning frameworks (Like ONNX, TensorFlow, PyTorch or Any Hardware Accelerator Software Pipeline Experience). 
  

Nice-to-Have:

• Knowledge in Python. 
  
• Experience in managing team size of 4 or more. 
  
• Experience Working in an Agile Environment. 
  
• Experience in using automated testing frameworks.