Experience with memory management, concurrency, and performance optimisation techniques
Understanding of compiler design principles, including Abstract Syntax Trees (AST), intermediate representations (IR) and optimisation passes
Familiar with computational graph optimization and execution strategies within AI inference engines
Requirements:
Developing highly efficient computation kernels and client-side infrastructure to accelerate the neural network inference / DSP pipeline
Collaborating with an international team of engineers and music lovers
Having a direct influence on systems, product, and architecture
Designing clean, extensible architectures for new framework features
Job description
This is a remote position.
C++ Backend Optimization Engineer
Audio AI / Music Technology
Remote working from anywhere
Join an international team of R&D Engineers working on one of the most exciting and popular music production tools on the market.
This is an ideal role for an experienced Backend Engineer who is looking for more freedom and responsibility, and likes to work with a team who Get Things Done. It's a great time to join the company as they continue to grow and release more innovative software for producers and song writers.
As a Backend Optimisation Engineer, you'll be responsible for developing highly efficient computation kernels and client-side infrastructure to accelerate the neural network inference / DSP pipeline, already renowned for its extreme efficiency.
If you're looking for a role where you can have a direct influence on systems, product and architecture, this is it.
Remote working from anywhere, collaborating with an international team of engineers and music lovers.
Requirements
Essential:
Strong skills in Modern C++ (C++17/20/23)
Experience with memory management, concurrency, and performance optimisation techniques
Understanding of compiler design principles, including Abstract Syntax Trees (AST), intermediate representations (IR) and optimisation passes
Familiar with computational graph optimization and execution strategies within AI inference engines
Understanding of basic performance profiling and tuning methodologies (analysing cache misses, memory bandwidth, thread contention)
Able to design clean, extensible architectures for new framework features
Bonus points for:
3+ years of professional or strong academic/open-source experience related to AI compilers (TVM, MLIR, XLA) or open-source AI inference engines
Experience with concurrency libraries (oneTBB or OpenMP)
Experience with GPU programming using Vulkan, Metal, CUDA, SYCL, or OpenCL
