Member of Technical Staff, Kernel Engineering
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Before you apply
SalarySGD 200k – SGD 400k
LocationSingapore
Work modeon-site
Typefull-time
SeniorityLead
DepartmentResearch & Engineering
EquityIncluded
Company size51–200 people
PostedJun 24, 2026 · 4mo ago
Verified live3d ago
At a glanceSummarised by Seekless from the posting.
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
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)
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
We sponsor visas on a case-by-case basis
Skills
CUDA
CuTeDSL
Triton
TileLang
Pallas
C++
Python
Nsight
rocprof
FlashAttention
LLVM
MLIR
XLA
Benefits
Medical coverage
Dental coverage
Vision coverage
About the company
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:
Nice to have
•
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.