What are the responsibilities and job description for the Compiler Engineer (GPU Backend) position at Oxmiq Labs?
Compiler Engineer (GPU Backend)
Experience - 5 Years
Key Responsibilities
- Design and implement compiler infrastructure for GPU-accelerated workloads, including lowering pipelines and code generation backends.
- Develop and optimize MLIR-based compilation passes targeting Oxmiq hardware IP and GPU architectures.
- Build and maintain LLVM backend components, including instruction selection, register allocation, and target-specific optimizations.
- Work with architecture and hardware teams to translate performance requirements into efficient code generation strategies.
- Optimize compiler output for throughput, latency, and memory efficiency across GPU workloads.
- Collaborate with kernel engineers to support Triton-based programming models and custom op lowering.
- Participate in design reviews, code reviews, and architecture discussions.
- Support performance profiling, benchmarking, and root cause analysis for compiler-generated code.
Required Qualifications
- 5 years of compiler engineering experience with a focus on GPU or hardware accelerator backends.
- Strong expertise in C and LLVM, including familiarity with the LLVM IR and pass infrastructure.
- Hands-on experience with MLIR, including dialect design, conversion passes, and progressive lowering.
- Proven experience with GPU programming models, including CUDA.
- Solid understanding of code generation concepts including instruction scheduling, vectorization, and memory hierarchy optimization.
- Familiarity with GPU architecture fundamentals including thread execution models, memory systems, and occupancy constraints.
- Experience working with Triton or similar high-level GPU kernel frameworks.
- Strong debugging and performance analysis skills.
- Hands-on experience with Claude Code or equivalent AI-assisted development workflows.
Preferred Qualifications
- Experience developing compiler backends for custom AI accelerators, NPUs, or GPU IP.
- Familiarity with kernel fusion, operator tiling, and auto-tuning frameworks.
- Exposure to quantization, mixed-precision, or sparsity-aware compilation techniques.
- Experience with performance modeling and roofline analysis for GPU workloads.
- Contributions to open-source compiler projects such as LLVM, MLIR, or Triton.
- Scripting experience in Python for build automation, testing, or performance benchmarking.
Education
- BS/MS/PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field.