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