• Own IT systems engineering for corporate infrastructure, designing, implementing, and operating reliable, secure systems that support Lucidworks operations and meet enterprise IT standards. • Design and improve Lucidworks IT infrastructure, including identity management, endpoint management, network architecture, and SaaS integration, with a focus on scalability, security, and operational efficiency. • Set a high bar for system reliability and configuration quality through rigorous documentation, peer review of infrastructure changes, and active reduction of technical debt in owned systems. Raise design and security issues proactively. • Own the operational health of systems in your domain, contributing to SLA/uptime targets, maintaining monitoring and alerting, participating in incident response, and driving post-incident improvements that prevent recurrence. • Partner with IT leadership, security, and business stakeholders to plan and deliver infrastructure projects, communicating technical restraints, risks, and tradeoffs clearly to both technical and non-technical audiences. • Apply Gen AI tools to accelerate systems administration, scripting, and documentation work, with the judgment to critically evaluate AI-generated output and to keep sensitive credentials and infrastructure details out of AI tools. • Provide guidance and knowledge-sharing to other IT staff and act as an escalation point for complex systems issues. Contribute to a culture of documentation, ownership, and continuous improvement.

• Analyze workflows • Document system requirements • Collaborate with various stakeholders • Ensure effective implementation of technology solutions • Focus on continuous improvement and system optimization.

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. 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. - 100% remote work opportunity. - Long-term, multi-year engagement aligned to the Bright Vision SOW delivery roadmap.

• Act as the primary bridge between business units, IT teams, and external product vendors; • Translate complex financial business processes and integration needs into clear, actionable functional and non-functional requirements; • Provide expert design recommendations, own system blueprints for projects and enhancements, and maintain the enterprise integration catalog; • Support rigorous testing efforts and create comprehensive application and system documentation across the organization.