CUDA Engineer

Job not on LinkedIn

🔥 19 hours ago

🇬🇧 United Kingdom – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

👷🏻‍♀️ Engineer

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Fuse Energy

WebsiteLinkedInAll Job Openings

11 - 50 employees

⚡ Energy

💰 Venture Round on 2022-09

Energy

Fuse Energy is an energy company that simplifies the process of switching electricity suppliers and provides real-time billing information. They offer competitive tariffs in the UK and operate solar and wind projects, reinvesting profits into renewable energy initiatives worldwide. Fuse Energy aims to provide customers with transparent billing and easy switching through their app, while promoting sustainability.

📋 Description

• Write and optimise custom CUDA kernels for core transformer inference operations. • Profile kernels to identify and eliminate bottlenecks in occupancy, memory throughput, and warp divergence. • Apply kernel fusion to reduce memory round-trips and launch overhead across inference pipelines. • Optimise memory access patterns and manage the memory hierarchy for maximum bandwidth utilisation. • Implement quantisation-aware kernels and mixed-precision arithmetic to reduce latency and memory footprint. • Build and tune caching mechanisms for efficient autoregressive decoding. • Tune kernel launch configurations for target GPU architectures. • Benchmark kernels against existing baselines and drive measurable throughput and latency improvements. • Write tests for CUDA code to catch performance and correctness regressions. • Maintain internal CUDA libraries and contribute to team coding standards and documentation.

🎯 Requirements

• 4+ years writing production CUDA code, with a track record of shipping performance-critical kernels. • Deep understanding of GPU microarchitecture, warps, occupancy, register pressure, and memory hierarchy. • Strong CUDA C++ skills, including streams and asynchronous execution. • Hands-on experience profiling to diagnose compute-bound vs. memory-bound bottlenecks. • Experience with kernel fusion, memory coalescing, and avoiding warp divergence. • Experience writing quantised and mixed-precision kernels. • Solid grasp of parallel algorithm design and numerical precision tradeoffs. • Nice to Have • Experience with transformer/attention-style kernels or autoregressive decoding. • Experience building high-performance GPU libraries from scratch. • Background in HPC or other latency-critical performance engineering. • Exposure to multi-GPU or multi-node kernel-level optimisation. • Comfortable reading PTX/SASS to validate kernel efficiency.

🏖️ Benefits

• Competitive salary and an equity sign-on bonus. • Biannual bonus scheme. • Fully expensed tech to match your needs. • Breakfast and dinner allowance for office based employees.