Lead GPU AI Infra Engineer — Perception & Planning

BLACK SESAME TECHNOLOGIES (SINGAPORE) PTE. LTD.; Singapore; On-site; SGD 80,000 - 120,000; Full time

Job summary

A tech company in Singapore is seeking a highly skilled engineer to design and optimize the GPU/AI infrastructure for their Perception & Planning stack. The ideal candidate will have a strong ML/CV background and expert coding skills in C++ and Python. Responsibilities include architecting large-scale training pipelines, profiling and eliminating bottlenecks, implementing key performance components, and leading distributed training efforts. A Master's or Ph.D. in a relevant field is required, alongside experience with GPU profiling and tuning.

Qualifications

• Master’s or Ph.D. in Computer Science, Electrical/Computer Engineering, or related technical discipline.
• Strong foundation in ML/CV with proven experience in GPU/AI infrastructure and performance optimization.
• Hands-on experience with GPU profiling and tuning.

Responsibilities

• Architect and optimize large-scale training pipelines.
• Profile end-to-end pipelines and eliminate bottlenecks.
• Implement performance-critical components in CUDA/C++.
• Tune GPU utilization and memory hierarchy.
• Drive model conversion and deployment workflows.
• Lead distributed training scaling and orchestration.
• Build reliability and observability into systems.
• Maintain benchmarks and profiling reports.

Skills

Expert-level coding in C++

Expert-level coding in Python

Strong foundation in ML/CV

GPU profiling

Performance optimization

Education

Master’s or Ph.D. in Computer Science, Electrical/Computer Engineering

Tools

CUDA

ONNX

TensorRT

NCCL

Job description

A tech company in Singapore is seeking a highly skilled engineer to design and optimize the GPU/AI infrastructure for their Perception & Planning stack. The ideal candidate will have a strong ML/CV background and expert coding skills in C++ and Python. Responsibilities include architecting large-scale training pipelines, profiling and eliminating bottlenecks, implementing key performance components, and leading distributed training efforts. A Master's or Ph.D. in a relevant field is required, alongside experience with GPU profiling and tuning.