GPU Systems Engineer

Responsibilities

We are representing a confidential technology organization seeking a GPU Systems Engineer to design and optimize compute-intensive workloads on modern accelerator hardware. In this role, you will:

• Design and implement high-performance CUDA kernels for AI and high-performance computing 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, working at the boundary between ML frameworks and underlying accelerator codegen
• 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
• Document performance characteristics, design decisions, and tuning playbooks for internal teams
• Stay current with GPU architecture, CUDA evolution, and emerging accelerator technologies

What We're Looking For

Required qualifications:

• Bachelor's or Master's degree in Computer Science, Computer Engineering, or a related field
• 6+ 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 environments
• 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

Location: Remote (United States)

Employment type: Full-time, W2

Visa status: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. Our client is unable to sponsor new H-1B visa petitions for this position.

How We Work With You

Our talent team at Get A Job.ai will guide you through the entire process. When you apply through our platform, one of our recruiters will screen your background and experience. If there's a strong match, we'll submit your profile to our client for consideration. Please note that we ask candidates not to contact the client directly—we'll manage all communication and coordination on your behalf to ensure the best possible experience.

Pay

Pay via Get A Job.ai: $100,000–$150,000 annually

Equal Employment Opportunity
Get A Job.ai is committed to equal employment opportunity for all applicants regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic under applicable law.