Member of Technical Staff, Kernel Engineering
At a glance
Senior Embedded & Firmware role at Inferact. Singapore · full-time · $200,000–$400,000 base.
techroles summary, based on the employer's posting.
What you'll do
- Write GPU kernels and low-level code to improve vLLM inference speed across accelerator hardware
- Tune performance for new chips by working directly with hardware vendor teams
- Use profiling and benchmarks to find bottlenecks and measure optimization gains
What you bring
- A computer science, engineering or similar degree, or equivalent practical experience
- Substantial experience building CUDA kernels or using an equivalent such as Triton or CuTeDSL
- Strong grasp of GPU memory, warp scheduling, tiling and tensor cores, plus high-performance C++ and Python skills
- Hands-on use of profiling tools such as Nsight or rocprof to guide performance work
Who this fits
This senior, hands-on kernel engineering role suits someone focused on GPU performance and inference optimization. The role is based on-site in Singapore. Visa sponsorship is considered case by case.
From the employer
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 performance 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.
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, Pallas).
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.
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