NeuReality’s software department is looking for an experienced and highly motivated Compiler Lead to join us and contribute to the development of NeuReality’s compiler tools. This is an exciting opportunity to work with highly talented engineers and be a part of product innovation on cutting-edge compilation technologies in Artificial Intelligence/Deep Learning domain. If you are an excellent, bright technologist with a passion to make a difference – consider joining our group.
As a lead in our group, you will be responsible for designing a deep learning compiler stack that converts AI applications developed in deep learning frameworks such as PyTorch/TensorFlow into code suitable for execution on custom-built NeuReality’s hardware.
Our group is responsible for the development of NeuReality’s AI platform software, including AI architecture, workloads, optimization algorithms, and deployment tools. The development environment is mostly based on Python, PyTorch, and TensorFlow frameworks within the deep learning, computer vision, and audio processing domains. We get to see our code running in the most advanced algorithms and use cases which are developed both by NeuReality and our customers.
- Develop a programming model and tools that will provide a quantum leap in AI capabilities and performance.
- Develop new optimization techniques and algorithms to efficiently map AI workloads onto our state-of-the-art system.
- Use state-of-the-art open-source technologies such as LLVM and MLIR and work closely with the open-source community.
- Will lead the team of compiler engineers.
- BSc/MSc in Computer Science or Computer Engineering from the accredited university
- Knowledge of C/C++ and Python
- Expertise with compiler technologies, such as LLVM/OpenCL/MLIR
- Knowledge of OOP, design patterns, and solid software development principles
- At least 5 years of compiler development experience
- At least 2 years of team management experience
- Excellent team player with strong communication skills in verbal and written English