GPU acceleration engineer

GPU Acceleration Engineer

• Calculation Engine 🎯 Main Mission Massively accelerate the sparse calculation engine of a UK SaaS B2B
• Enterprise Planning & Analytics company by porting critical algorithms from Rust/C++ to GPU (CUDA). Transform currently impossible calculations (requiring thousands of years of CPU time) into operations achievable in minutes. 📊 Context UK SaaS B2B
• Enterprise Planning & Analytics company manages planning models reaching 64 quadrillion cells with billions of time periods. Our Hyperblock/Polaris engine is currently limited by: Legacy CPU architecture (Java/Rust/C++) Memory constraints on massive sparse structures Prohibitive calculation times on complex scenarios Objective : Achieve performance gains of 100x to 1000x via GPU offloading. 🔧 Main Responsibilities GPU Offloading Port existing Rust/C++ algorithms to CUDA/GPU Identify and extract critical calculation paths to accelerate Optimize sparse matrix operations for GPU architecture Develop performant Rust ↔ CUDA wrappers Benchmark and validate performance gains Memory Optimization Design GPU memory management strategies for massive datasets Implement efficient patterns for sparse structures Optimize CPU ↔ GPU memory transfers Manage GPU memory limitations on large-scale calculations Technical Collaboration Work with engineering team on integration Document GPU porting patterns Participate in code reviews and design reviews Train the team on GPU best practices 💻 Technical Stack Languages (in order of importance) CUDA
• Primary GPU development Rust
• Source language for algorithms to port C++
• Legacy components and CUDA interoperability (Java - platform context, no dev required) Key Technologies NVIDIA CUDA (toolkit, libraries: cuBLAS, cuSPARSE) Rust (ownership model, unsafe blocks, FFI) GPU Programming (kernels, memory hierarchy, optimization) Sparse Matrix Operations (compression, storage formats) Profiling Tools (nvprof, Nsight, perf) ✅ Required Profile Essential Skills GPU & CUDA (Essential) ✅ Significant CUDA programming experience (3+ years) ✅ Mastery of GPU kernel optimization ✅ Deep knowledge of NVIDIA GPU architecture (memory hierarchy, warps, occupancy) ✅ Experience with sparse calculations on GPU (cuSPARSE or equivalent) Rust (Essential) ✅ Production Rust development ✅ Mastery of ownership and borrowing system ✅ Experience with unsafe Rust and FFI (Foreign Function Interface) ✅ Ability to analyze and refactor existing Rust code C++ (Required) ✅ Modern C++ (C++11/14/17) ✅ C++ ↔ CUDA integration ✅ Templates and metaprogramming (asset) Algorithms (Required) ✅ Data structures for scientific computing ✅ Sparse matrix algorithms (CSR, COO, etc.) ✅ Performance optimization and profiling ✅ Parallelization and concurrency concepts Highly Valued Experience 🎯 Documented CPU → GPU porting projects 🎯 HPC experience (supercomputers, GPU clusters) 🎯 Memory optimization for large-scale datasets 🎯 Scientific computing or numerical simulation 🎯 Rust interop with other languages (C/C++/Python) 📍 Working Arrangements Location & Travel 100% remote (France/Europe base preferred) Occasional travel to London Frequency: ~1 week/month for team sprints Project kickoff + key reviews Intensive collaboration sessions Start date : As soon as possible