Parallel Computing Engineer

Parallel Computing Engineer – RemoteBright 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: Parallel Computing EngineerLocation: 100% Remote (U.S.)Position Type: Full-time, Direct W2Salary Range: $100,000–$150,000 AnnuallyExperience Required: 6+ yearsSponsorship: 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 SummaryWe 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.Key ResponsibilitiesDesign and implement high-performance CUDA kernels for compute-intensive workloads across AI and HPC use casesProfile and optimize GPU code using tools such as Nsight Systems, Nsight Compute, and CUDA profilersTune memory access patterns, occupancy, register usage, and shared memory utilization for peak performanceDevelop highly optimized libraries for linear algebra, attention, and other ML primitivesOptimize multi-GPU and multi-node training using NCCL, RDMA, and high-performance networkingImplement custom operators and fused kernels in PyTorch, JAX, or TritonCollaborate with ML engineers to identify performance bottlenecks in training and inference pipelinesDevelop benchmarks and regression tests to safeguard performance over timeEvaluate new GPU architectures and feature sets, and advise on adoption strategyContribute 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 aloneOptimize memory hierarchy usage across HBM, L2, shared memory, and registersImplement mixed-precision and quantized compute paths that maximize accelerator throughput while preserving numerical fidelity within bounds acceptable for the target workloadsDocument performance characteristics, design decisions, and tuning playbooks for internal teamsStay current with GPU architecture, CUDA evolution, and emerging accelerator technologiesRequired QualificationsBachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related fieldSix or more years of experience in GPU programming and performance engineeringDeep expertise in CUDA C/C++ and GPU programming modelsStrong understanding of modern GPU architectures, memory hierarchies, and execution modelsHands-on experience profiling and optimizing GPU workloads in productionFamiliarity with NCCL, MPI, and high-performance interconnect technologiesExperience integrating custom kernels into ML frameworksStrong C++ skills and familiarity with modern systems programming practicesSolid grounding in linear algebra and numerical methodsStrong communication and collaboration skills with research and engineering teamsPreferred QualificationsExperience with Triton, CUTLASS, or other GPU kernel authoring frameworksFamiliarity with TensorRT, FasterTransformer, or vLLM internalsExposure to compiler infrastructure such as LLVM or MLIROpen-source contributions to GPU or ML performance librariesExperience with large-scale distributed training infrastructureHow to ApplyWould you like to know more about this opportunity? For immediate consideration, please send your resume to jaya@bvteck.com or contact us at (908) 505-3545. Learn more about Bright Vision Technologies at www.bvteck.com.Bright Vision Technologies is an Equal Opportunity Employer.Equal Employment Opportunity (EEO) StatementBright 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 JazzHRLLuU2Y7oqr