Are you ready for new challenges?
Are you eager to:
- Contribute to development of cutting edge embedded platforms utilized by more than billion of users?
- Work on ML compiler – one of the hottest Embedded topics today?
- Improve your experience in compiler technology, vectorization and optimization techniques?
If your answer is YES to any of these questions, the HTEC Embedded team is the right place for you. We are seeking ambitious, bright and innovative engineers to work on development of a platform that enables execution of highly demanding Machine Learning solutions in different application areas (eXtended Reality, Natural Language Processing, Translations, Computer Vision, Autonomous Driving, etc). Job activities span the whole product life cycle from early R&D to commercial deployment.
Key responsibilities
- Research and development of the different components of a machine learning compiler based on performance and code-size requirements
- Analyze software requirements and implement software solutions best suited for given platforms.
- Analyze and identify system level integration issues, interface with the software development, integration and test teams.
- Required qualifications
Minimum Qualifications
- Bachelor’s degree in Engineering, Computer Science, or related field.
- Hands-on experience with C/C++ Programming Language
- Willingness to work on low-level software components interfacing with hardware and simulators
- Experience with Object Oriented Design and TDD development
- Experience with version control and issue management tools
- Strong experience with software profiling techniques.
Preferred Qualifications
- Academic or professional experience with compiler development (LLVM experience is a plus).
- Knowledge of the structure and function of the compiler internals.
- Experience in using C++ 14/17 (advanced features)
- Experience with hardware accelerators
- Hands-on experience of writing SIMD and/or multi-threaded high-performance code, as well as target specific optimizations
- Knowledge of machine learning frameworks internals and neural network architectures is a plus.
