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.