Senior Compiler Engineer

Job description

About OLIX AI is growing faster than any technology in history and the explosion in demand has created a massive infrastructure gap; we can no longer build chips or power stations fast enough to keep up. The industry is still leaning on a ten-year-old hardware blueprint that has reached its limit. A new paradigm that is faster and more efficient will be the biggest economic opportunity of the next century and create the most important company of the next decade. The OLIX Decode Accelerator 1 (DX-1) is the first accelerator architected specifically for decode. Rack-scale co-design of logic, data movement, packaging, optics and interconnect enables a step change in system level performance. The Role We’re searching for Senior Compiler Engineer to join the team building the ML backend (compiler, run-time, and debugger) for our next-generation hardware that connect PyTorch, Tensorflow, JAX, and MXNet down to our low-level kernel drivers. Your mission will be to create seamless support for a broad ecosystem of large AI models, and ensure we are pushing the limits of their performance envelope by closely partnering with hardware and modelling teams to understand device trade-offs. Responsibilities Project Ownership: Own and deliver projects on your team's roadmap that unlock key high-impact technical and business milestones that drive the success of Olix. Architect & Build: Design and implement our compiler, runtime, and debugger for PyTorch, TensorFlow, JAX, and MXNet on custom hardware. Optimise Performance: Apply advanced techniques (layout, fusion, scheduling, tiling) to eliminate bottlenecks and maximise throughput. Mentor & Encourage Standards: Lead code reviews, coach peers, and uphold best practices in ML backend and performance engineering for your team. Guide Team Direction: Stay ahead of GPU, AI accelerator trends; propose and prototype innovations. Skills & Experience 5+ years of experience in software engineering with a focus on C/C++ programming. Deep expertise in ML framework internals, compilers, low-level programming, and optimisation techniques. Deep expertise in optimising Tensorflow, PyTorch or JAX deep learning models. Deep expertise with multiple toolchains like LLVM, OpenXLA/XLA, MLIR, TVM. Practical experience applying machine learning in high-performance computing contexts. Strong problem-solving skills and the ability to think critically…