GPU Software Engineer (CUDA)

GPU Software Engineer (CUDA)– Remote
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.
Job Title: GPU Software Engineer (CUDA)
Location: 100% Remote (U.S.)
Position Type: Full-time, Direct W2
Salary Range: $100,000–$175,000 Annually
Experience Required: 10+ years
Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.
Job Summary:
We are seeking a GPU Software 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.
Key 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
Required Qualifications
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
How to Apply
Would you like to know more about this opportunity? For immediate consideration, please send your resume to venkat.r@bvteck.com or contact us at (908) 505-3899. Learn more about Bright Vision Technologies at www.bvteck.com.
Bright Vision Technologies is an Equal Opportunity Employer.
Equal Employment Opportunity (EEO) Statement
Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.
BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.
Powered by JazzHR
9XRdRyBkle

Work arrangement
Yes