• Desarrollar servicios de backend usando Python y .NET/C# • Integrar marcos basados en LLM como LangGraph, AutoGen, etc. • Colaborar en flujos de trabajo impulsados por IA y arquitecturas escalables • Construir frontales React para aplicaciones impulsadas por IA

Role Description We are seeking a GPU Software Engineer (CUDA) 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. 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. Requirements - 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. Benefits - Competitive base salary commensurate with experience, plus benefits. - Long-term, multi-year engagement aligned to the Bright Vision SOW delivery roadmap. - 100% remote work opportunity.

• Design, develop, and maintain our backend systems and services • Lead technical discussions and help define the architecture and technical direction of the backend • Remain actively involved in coding, code reviews, and solving complex technical challenges • Oversee the development and maintenance of our GraphQL API, ensuring scalability, performance, and reliability • Support and mentor engineers through technical guidance, knowledge sharing, and feedback • Help prioritize work, identify blockers, and drive projects through to delivery • Manage deployments and contribute to infrastructure improvements in our Kubernetes environment on GKE • Work closely with Product, Design, QA, and Engineering teams to deliver high-quality features and a great user experience • Proactively identify opportunities for product and technical improvements based on business and user needs • Help establish and maintain engineering best practices, code quality standards, and development processes • Foster a collaborative and inclusive team environment while working with colleagues across different cultures and time zones

• You will be working with the high-caliber team in some of the below areas. • Noz, AI teammate: agent orchestration, tool execution, and the APIs that stream results back into the product • SigNoz MCP server that exposes observability data to LLM agents, so any agent can query a user's logs, metrics, and traces • The hard parts of agent backends: long-running execution, security, streaming over SSE, tool calling, and session state across DBs • LLM/GenAI observability, so teams (including us) can monitor their own AI apps with OpenTelemetry