• Lead the design, development, and roadmap of AI-Perf, defining benchmarking methodologies, performance metrics, and reproducible experimental workflows • Build scalable and high-performance features to measure latency, throughput, and efficiency across AI models and distributed systems • Partner with AI researchers, platform teams, and engineers to translate experimental challenges into robust, user-friendly performance tooling • Integrate AI-Perf with the Dynamo Inference Stack, other NVIDIA inference stacks, and open-source inference frameworks, delivering end-to-end performance insights for researchers and production users

• Bridging gaps between business processes and PLM functionality & capabilities. • Oversee the implementation and optimization of PLM tools, including but not limited to Siemens Teamcenter, Easy Plan, Manufacturing Process Planner (MPP), Teamcenter Quality, and BCT Inspector. • Drive the integration of PLM solutions with other business systems such as ERP, MES, and CAD to enable a seamless digital thread across the enterprise. • Act as the subject matter expert in Teamcenter Business Modeler IDE, leading the configuration and customization of workflows, data models, and process automation to optimize manufacturing operations. • Stay informed on emerging manufacturing trends, including Industry 4.0, additive manufacturing, automation, and digital twins, and identify opportunities to incorporate these technologies into our PLM processes. • Translating customer requirements into a solution design. • Technical and functional problem solving. • Task Management with a strong follow-up discipline. • Meeting all budget, deliverable, and schedule targets. • Production and end user support for PLM Process Planning solutions. • Supporting integration with other enterprise solutions. • Working with internal customers and external suppliers, business partners, to architect and build system solutions for using the latest technologies to satisfy requirements. • Managing suppliers and partners to schedule and status resources required for solution development and implementation. • Work on multiple projects simultaneously with regular reporting in a gated process. • Author and/or provide input to system functional requirements, technical design documentation and test plans. • Ensuring compliance with RTX / PW policies & procedures.

• Desenvolvimento e manutenção de painéis e dashboards no PI Vision para monitoramento operacional e gerencial. • Levantamento e entendimento de requisitos junto às áreas de negócio e operação. • Configuração de telas, gráficos, tendências, alarmes e indicadores em tempo real. • Integração e validação de dados provenientes do sistema PI System. • Sustentação dos painéis existentes, atuando na correção de falhas e ajustes evolutivos. • Monitoramento de performance e disponibilidade dos dashboards e ativos monitorados. • Atendimento de chamados, análise de incidentes e suporte aos usuários finais. • Criação de documentação técnica e procedimentos operacionais. • Apoio em testes, homologações e publicação de novas versões dos painéis. • Garantia de padronização visual, governança e boas práticas na construção dos dashboards.

Role Description We are seeking a GPU Systems 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. 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 [email protected] or contact us at (908) 505-3544. Learn more about Bright Vision Technologies at www.bvteck.com . 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.