Founding GPU Engineer

Fuse Energy

Founding GPU Engineer at Fuse Energy developing GPU-accelerated software for data centers. Focusing on workload scheduling and optimization in energy-intensive environments.

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

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in CUDA programming and GPU performance optimization, with a strong focus on multi-GPU scaling and energy-efficient computing strategies. Proficient in collaborating with cross-functional teams to integrate custom kernels and improve system performance.

Highest-signal resume keywords

CUDA ProgrammingGPU Performance OptimizationC++ ProficiencyMulti-GPU ScalingPerformance Profiling Tools

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

CUDAC++PythonPerformance ProfilingMemory OptimizationKernel FusionParallel Algorithm DesignNCCLMPINVLink

Tools & Technologies

Nsight SystemsNsight ComputeKubernetesSlurmCustom ML Frameworks

Industry Keywords

GPU ArchitectureData Center InfrastructureEnergy PricingSustainabilityHigh-Performance Computing

Tech Stack

Tools & technologies

Distributed SystemsKubernetesNode.jsPython

About the role

Key responsibilities & impact

Design, implement, and optimise CUDA kernels for high-throughput, latency-sensitive workloads.
Profile and tune GPU performance across compute, memory bandwidth, and interconnect (NVLink/PCIe) bottlenecks.
Build tooling to correlate GPU cluster power draw and utilisation with real-time energy pricing and grid signals.
Optimise multi-GPU and multi-node scaling using NCCL, MPI, or similar communication libraries.
Work with data center infrastructure teams on power capping, dynamic voltage/frequency scaling, and workload scheduling strategies that reduce energy cost and carbon intensity.
Collaborate with ML/systems engineers to integrate custom kernels into training/inference pipelines.
Benchmark against CPU/GPU baselines and drive continuous performance improvements.
Contribute to internal libraries, documentation, and best practices for GPU performance engineering.

Requirements

What you’ll need

4+ years of experience writing production CUDA code, or equivalent strong project/industry experience.
Deep understanding of GPU architecture (SMs, warps, memory hierarchy, occupancy).
Proficiency in C++ and CUDA; experience with Python for tooling/orchestration.
Experience with performance profiling tools (Nsight Systems/Compute).
Familiarity with multi-GPU/multi-node scaling (NCCL, MPI, RDMA/InfiniBand).
Strong grasp of memory optimisation, kernel fusion, and parallel algorithm design.
Comfortable working across the stack from low-level kernels to system-level infrastructure.
**Nice to Have**
Experience with Triton, cuDNN, cuBLAS, or custom ML inference/training frameworks.
Exposure to data center power/thermal management or demand-response systems.
Background in HPC, quantitative finance, or large-scale distributed systems.
Familiarity with Kubernetes/Slurm for GPU cluster orchestration.
Interest or experience in energy markets, grid systems, or sustainability-focused compute.

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.