Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware—a position that took years to build.

About the Role

We're looking for a kernel engineer to squeeze every FLOP out of modern accelerators. You'll write the kernels and low-level optimizations that make vLLM the fastest inference engine in the world. Your code will run on hundreds of accelerator types, from NVIDIA GPUs to emerging silicon. When hardware vendors develop new chips, they integrate with vLLM. You'll work directly with these teams to ensure we're extracting maximum performance from every generation of hardware.

Inferact has three kernel roles. If your kernel work is mostly on Google TPUs (Pallas, JAX), apply to TPU Kernel Engineering. If it is mostly on AMD GPUs (HIP, CK, AITER), apply to AMD GPU Kernel Engineering. For NVIDIA GPUs and cross-platform kernel work, apply to Kernel Engineering. Apply to one; we route you to the best fit.

Skills and Qualifications

Minimum qualifications:

Bachelor's degree or equivalent experience in computer science, engineering, or similar.

Deep experience writing CUDA kernels or equivalent (CuTeDSL, Triton, TileLang).

Strong understanding of GPU architecture: memory hierarchy, warp scheduling, tiling, tensor cores.

Proficiency in C++ and Python with demonstrated ability to write high-performance code.

Experience with profiling tools (Nsight, rocprof) and performance optimization methodologies.

Obsession with benchmarks and squeezing every percentage point of speedup.

Preferred qualifications:

Experience with ML-specific kernel optimization (FlashAttention, fused kernels).

Knowledge of quantization techniques (INT8, FP8, mixed-precision).

Familiarity with multiple accelerator platforms (NVIDIA, AMD, TPU, Intel).

Experience with compiler technologies (LLVM, MLIR, XLA).

Bonus points if you have:

Kernel-related contributions to vLLM or other inference engine projects.

Contributions to open-source GPU, ML systems, or compiler optimization projects

Written deep technical blogs on GPU optimization.

Logistics

Location: This role is based in Singapore.

Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is S$200,000 to S$400,000 annually + equity.

Visa sponsorship: We sponsor visas on a case-by-case basis.

Benefits: Inferact offers a generous benefits package, including medical, dental, and vision coverage.