CUDA Engineer

Fuse Energy

CUDA Engineer writing and optimizing performance-critical GPU code at a renewable energy startup. Focusing on high-performance compute infrastructure to support AI applications.

Posted 7/19/2026full-timeRemote • 🇬🇧 United KingdomMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in writing and optimizing CUDA kernels for transformer inference operations, with a strong focus on performance-critical applications and memory management. Proficient in profiling, benchmarking, and implementing advanced techniques such as kernel fusion and quantization-aware kernels to enhance throughput and reduce latency.

Highest-signal resume keywords

CUDA C++ ProgrammingKernel OptimizationGPU Microarchitecture UnderstandingPerformance ProfilingQuantization-Aware Kernels

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

CUDA ProgrammingKernel FusionMemory CoalescingMixed-Precision ArithmeticParallel Algorithm DesignPerformance BenchmarkingAsynchronous ExecutionOccupancy ManagementLatency ReductionMemory Hierarchy Management

Tools & Technologies

CUDA LibrariesHPC ToolsPTX/SASS

Industry Keywords

Transformer InferenceAutoregressive DecodingHigh-Performance ComputingMulti-GPU Optimization

Tech Stack

Tools & technologies

Node.jsSCSS

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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.