Systems Software Engineer - Kernels & IPC

As a Software Engineer at our client, a leading global technology company specializing in semiconductor manufacturing, inspection systems, and advanced engineering solutions, your primary responsibility will be to design and develop high-performance distributed software systems for HPC environments on Linux using C++. You will focus on building and optimizing Linux-based C/C++ components for compute-intensive workloads, implementing parallel computing frameworks, containerizing and orchestrating workloads, and profiling, debugging, and tuning performance. Additionally, you will collaborate with hardware, algorithms, and systems teams to drive integrated solutions and mentor team members on performance optimization and system debugging. Key Responsibilities: - Design and develop high-performance distributed software systems for HPC environments - Build and optimize Linux-based C/C++ components for compute-intensive workloads - Implement parallel computing frameworks using MPI, OpenMP, UCX, or similar - Containerize and orchestrate workloads using Docker/Singularity with Kubernetes or SLURM - Profile, debug, and tune performance using tools like VTune, Nsight, perf, and gdb - Collaborate with hardware, algorithms, and systems teams for integrated solutions - Drive code quality, architecture discussions, and engineering best practices - Mentor team members on performance optimization and system debugging Qualifications: - 48 years of experience in software development (HPC / distributed systems preferred) - Minimum 4 years of hands-on experience in C++ and Linux development - Strong understanding of IPC, socket programming, and Linux kernel concepts - Experience in multi-threading, concurrency, and systems-level programming - Hands-on experience with performance profiling and debugging tools (VTune, Nsight, perf, gdb) - Experience with Docker/Singularity and orchestration frameworks (Kubernetes/SLURM) - Knowledge of CPU/GPU architectures and distributed computing systems - Experience with MPI, OpenMP, UCX, SHMEM, or similar parallel programming models - Exposure to GPU computing (CUDA / ROCm) - Familiarity with ML/Deep Learning pipelines - Proficiency in Python and Bash scripting - Experience with microservices, observability tools, or large-scale deployments As a Software Engineer at our client, a leading global technology company specializing in semiconductor manufacturing, inspection systems, and advanced engineering solutions, your primary responsibility will be to design and develop high-performance distributed software systems for HPC environments on Linux using C++. You will focus on building and optimizing Linux-based C/C++ components for compute-intensive workloads, implementing parallel computing frameworks, containerizing and orchestrating workloads, and profiling, debugging, and tuning performance. Additionally, you will collaborate with hardware, algorithms, and systems teams to drive integrated solutions and mentor team members on performance optimization and system debugging. Key Responsibilities: - Design and develop high-performance distributed software systems for HPC environments - Build and optimize Linux-based C/C++ components for compute-intensive workloads - Implement parallel computing frameworks using MPI, OpenMP, UCX, or similar - Containerize and orchestrate workloads using Docker/Singularity with Kubernetes or SLURM - Profile, debug, and tune performance using tools like VTune, Nsight, perf, and gdb - Collaborate with hardware, algorithms, and systems teams for integrated solutions - Drive code quality, architecture discussions, and engineering best practices - Mentor team members on performance optimization and system debugging Qualifications: - 48 years of experience in software development (HPC / distributed systems preferred) - Minimum 4 years of hands-on experience in C++ and Linux development - Strong understanding of IPC, socket programming, and Linux kernel concepts - Experience in multi-threading, concurrency, and systems-level programming - Hands-on experience with performance profiling and debugging tools (VTune, Nsight, perf, gdb) - Experience with Docker/Singularity and orchestration frameworks (Kubernetes/SLURM) - Knowledge of CPU/GPU architectures and distributed computing systems - Experience with MPI, OpenMP, UCX, SHMEM, or similar parallel programming models - Exposure to GPU computing (CUDA / ROCm) - Familiarity with ML/Deep Learning pipelines - Proficiency in Python and Bash scripting - Experience with microservices, observability tools, or large-scale deployments