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