Parallel Computing Engineer

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role involves accelerating numeric and simulation kernels through GPU/CPU parallelism, memory-hierarchy tuning, and distributed execution across clusters. You’ll design scalable pipelines that maximize efficiency and throughput for large-scale computational workloads. Speed up numeric and simulation kernels through GPU/CPU parallelism. Optimize workloads via memory-hierarchy tuning and communication reduction. Scale computations with MPI, NCCL, and Slurm for distributed clusters. Profile and benchmark performance using nvprof and nsys. Build reproducible pipelines in Python, NumPy, and SciPy for HPC workflows. Collaborate with researchers and engineers to integrate HPC into production AI systems. Qualifications Background in computer science, high-performance computing, or applied mathematics. Experienced with GPU/CPU parallel programming using CUDA and OpenMP. Understanding of distributed execution frameworks and tools like MPI, NCCL, and Slurm. Proficient in Python with libraries like NumPy and SciPy for scientific computing. Experience profiling and optimizing workloads with tools like nvprof and nsys. Care about memory hierarchy, communication overhead, and scalability in parallel systems. Curious about how HPC techniques accelerate AI training, simulations, and scientific workloads. Requirements Design, optimize, and deploy parallel computing pipelines that accelerate numeric, simulations, and large-scale computations across GPUs, CPUs, and distributed clusters. Benefits Classified as an hourly contractor to Mercor. Paid weekly via Stripe Connect, based on hours logged. Part-time (20–30 hrs/week) with flexible hours—work from anywhere, on your schedule. Weekly Bonus of $500–$1000 USD per 5 tasks. Remote and flexible working style.