Parallel Computing Engineer

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.

Location: 100% Remote (U.S.)
Position Type: Full-time, Direct W2
Salary Range: $100,000–$150,000 Annually
Experience Required: 6+ years
Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply.

About the role

We are seeking a Parallel Computing Engineer with deep expertise in CUDA programming, GPU architecture, and high-performance computing to design and optimize compute-intensive workloads on modern accelerator hardware. This role focuses on extracting maximum performance from GPU platforms for AI training, inference, scientific computing, and high-throughput data processing workloads. The ideal candidate combines low-level systems mastery with strong software engineering practices, and has a track record of delivering measurable performance improvements on production GPU systems.

In this role you will work closely with cross-functional partners — product, design, engineering, operations, and business stakeholders — to translate ambiguous requirements into well-engineered solutions, and will be expected to raise the bar through code review, design review, and mentorship of more junior engineers. The successful candidate brings strong engineering discipline, a clear communication style, and a track record of shipping meaningful work that holds up well in production.

Responsibilities

• Design and implement high-performance CUDA kernels for compute-intensive workloads across AI and HPC use cases
• Profile and optimize GPU code using tools such as Nsight Systems, Nsight Compute, and CUDA profilers
• Tune memory access patterns, occupancy, register usage, and shared memory utilization for peak performance
• Develop highly optimized libraries for linear algebra, attention, and other ML primitives
• Optimize multi-GPU and multi-node training using NCCL, RDMA, and high-performance networking
• Implement custom operators and fused kernels in PyTorch, JAX, or Triton
• Collaborate with ML engineers to identify performance bottlenecks in training and inference pipelines
• Develop benchmarks and regression tests to safeguard performance over time
• Evaluate new GPU architectures and feature sets, and advise on adoption strategy
• Contribute to compiler-level optimizations for tensor programs where appropriate, working at the boundary between ML frameworks and underlying accelerator codegen to unlock performance not reachable through framework-level tuning alone
• Optimize memory hierarchy usage across HBM, L2, shared memory, and registers
• Implement mixed-precision and quantized compute paths that maximize accelerator throughput while preserving numerical fidelity within bounds acceptable for the target workloads
• Document performance characteristics, design decisions, and tuning playbooks for internal teams
• Stay current with GPU architecture, CUDA evolution, and emerging accelerator technologies

Requirements

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field
• Six or more years of experience in GPU programming and performance engineering
• Deep expertise in CUDA C/C++ and GPU programming models
• Strong understanding of modern GPU architectures, memory hierarchies, and execution models
• Hands-on experience profiling and optimizing GPU workloads in production
• Familiarity with NCCL, MPI, and high-performance interconnect technologies
• Experience integrating custom kernels into ML frameworks
• Strong C++ skills and familiarity with modern systems programming practices
• Solid grounding in linear algebra and numerical methods
• Strong communication and collaboration skills with research and engineering teams

Preferred Qualifications

• Experience with Triton, CUTLASS, or other GPU kernel authoring frameworks
• Familiarity with TensorRT, FasterTransformer, or vLLM internals
• Exposure to compiler infrastructure such as LLVM or MLIR
• Open-source contributions to GPU or ML performance libraries
• Experience with large-scale distributed training infrastructure

Pay

$100,000–$150,000 Annually