Parallel Computing Engineer (Train AI Models Part Time!)

accelerate numeric and simulation kernels

You’re a Great Fit If You

• Have a background in computer science, high-performance computing, or applied mathematics.
• Are experienced with GPU/CPU parallel programming using CUDA and OpenMP.
• Understand distributed execution frameworks and tools like MPI, NCCL, and Slurm.
• Are proficient in Python with libraries like NumPy and SciPy for scientific computing.
• Have experience profiling and optimizing workloads with tools like nvprof and nsys.
• Care about memory hierarchy, communication overhead, and scalability in parallel systems.
• Are curious about how HPC techniques accelerate AI training, simulations, and scientific workloads.
  

Primary Goal Of This Role

What You’ll Do

• 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.
  

Why This Role Is Exciting

• You’ll accelerate mission-critical AI and scientific workloads.
• You’ll work across the stack: hardware-level optimization, distributed execution, and HPC software engineering.
• You’ll directly impact the performance of large-scale AI models and simulations.
• You’ll be part of shaping how parallel computing powers the next generation of AI systems.
  

Pay & Work Structure

• You’ll be 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.