Role Description - 5+ years of Java + Spring Boot microservices experience - 3+ years deploying and operating services on Kubernetes (GKE or AWS) - Hands-on JVM profiling and heap analysis experience - Load testing experience - Experience diagnosing memory leaks and latency issues in production - Experience triaging OOMs and P99 spikes - Experience identifying and remediating security vulnerabilities (dependency/CVE scanning, patching) - Experience with GitHub pull requests, branch protection rules, and code review workflows - Strong understanding of Git branching strategies (GitFlow or Trunk-Based Development) - Experience resolving merge conflicts and maintaining release branches - Familiarity with GitHub Actions or other CI/CD pipelines for automated build, testing, and deployment Qualifications - Experience with GitHub Copilot for developer productivity - Experience with Infrastructure as Code repositories (Terraform, Helm Charts) - Experience managing Kubernetes deployment manifests using GitOps (ArgoCD or FluxCD) - Experience reviewing performance-related code changes and production hotfixes through GitHub Requirements - Minimum Compensation: USD 40,000 - Maximum Compensation: USD 140,000 - Compensation is based on actual experience and qualifications of the candidate. - The above is a reasonable and a good faith estimate for the role. - Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full-time employees. - This position is available for independent contractors. - No applications will be considered if received more than 120 days after the date of this post.

Role Description 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. - Work closely with cross-functional partners — product, design, engineering, operations, and business stakeholders — to translate ambiguous requirements into well-engineered solutions. - Raise the bar through code review, design review, and mentorship of more junior engineers. - Bring 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. - 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. - 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.

• Assist in the design, development, and maintenance of scalable, API-first solutions • Apply AI-assisted development practices to accelerate solution delivery • With guidance, convert well-defined concepts and ideas into solutions • Assist in analyzing and modifying existing business applications and processes • Diagnose and resolve routine problems in the maintenance and support of business applications • Interact with client and technical teams to define requirements and develop solutions • Provide input when reviewing new business application designs • Collaborate with the business and management teams to develop efficient plans, processes, timelines and work required

• Develop and maintain analyzer workflow logic within the ECL platform, owning the backend processes that drive analyzer operations end-to-end • Implement and extend workflow orchestration — managing analyzer state transitions, operational sequencing, and runtime event handling • Collaborate with the Analyzer Integration Engineer on the communication layer between ECL and connected analyzers • Support the DCU modernization effort by identifying workflow logic that can be extracted and repackaged into shared .NET libraries • Work with QA to ensure workflow correctness and reliability across analyzer types and edge cases • Participate in knowledge transfer from departing Hydra resources to preserve continuity of analyzer workflow behavior.