How to apply for Founding GPU Engineer

About Fuse Energy

Fuse Energy is a fully integrated energy company that develops its own solar and battery projects, builds hardware, trades power in real time, and sells directly to consumers. It has raised over $200M from investors including Balderton, Lakestar, Accel, Creandum, Lowercarbon, Ribbit, 20VC, Hummingbird, and Collaborative Fund. The company is now expanding into high-performance compute infrastructure at the intersection of energy and AI.

About the role

As Founding GPU Engineer, you will develop and optimise GPU-accelerated software for data centre systems. You will do low-level performance engineering for large-scale compute clusters and tie GPU workload behaviour to energy availability and grid demand. This role puts CUDA/GPU performance engineering at the centre of how Fuse schedules, cools, and balances power-dense GPU workloads against real-time grid conditions.

A typical day

A typical day may involve profiling GPU workloads, writing or optimising CUDA kernels, and working on scheduling logic that ties compute to energy availability and grid demand. You would coordinate with data centre and energy teams to balance power-dense workloads against real-time conditions. As a founding engineer, you would also help define the architecture and tooling for Fuse's high-performance compute infrastructure.

Who Fuse Energy is looking for

• Strong CUDA and GPU performance engineering experience, including low-level optimisation for large-scale compute clusters.
• Experience with data centre systems, GPU workload scheduling, cooling, or power management.
• Ability to work at the intersection of energy and AI, connecting GPU behaviour to grid conditions and energy availability.
• Comfortable as a founding engineer: self-directed, able to build from scratch, and willing to work remotely across the UK.

Tips for this application

• Highlight specific CUDA or GPU optimisation projects you have shipped, including cluster scale, performance gains, and power or cooling constraints.
• Explain how your GPU work relates to energy or grid demand. Fuse is not a typical AI company; show you understand the energy angle.
• Mention any experience with real-time systems, scheduling, or balancing compute against external constraints like power availability.
• Reference Fuse's integrated model: solar, batteries, hardware, trading, and AI. Show why a founding GPU role at an energy company appeals to you.
• Keep your application focused on low-level performance engineering. Avoid generic AI/ML research framing if your work is not systems-level.

What to cover in your cover letter

Focus on: (1) your hands-on CUDA/GPU performance engineering work and its scale; (2) how you have tied compute workloads to power, cooling, or grid constraints; (3) why Fuse's integrated energy-plus-compute approach fits your background; (4) your interest in being a founding engineer who builds GPU infrastructure from the ground up.

Research before applying

• Fuse Energy's funding history and investors, including Balderton, Lakestar, Accel, Creandum, Lowercarbon, Ribbit, 20VC, Hummingbird, and Collaborative Fund.
• Fuse's existing business: solar and battery development, hardware, grid infrastructure, real-time power trading, AI across the business, and distributed energy installation.
• The intersection of data centre electricity demand and grid conditions, especially how GPU workloads can be scheduled against energy availability.
• Any public statements or materials from Fuse about their high-performance compute infrastructure plans.

Likely interview topics

Based on the job description, expect questions about:

• Deep dive into a CUDA or GPU optimisation you led: what was slow, what you changed, and what the measured impact was.
• How you would schedule power-dense GPU workloads against real-time grid conditions and energy availability.
• Your approach to cooling and power balancing in large-scale compute clusters.
• How you would design GPU-accelerated software for a data centre system from scratch as a founding engineer.
• Why Fuse Energy, and how you see GPU performance engineering fitting into an integrated renewable energy company.

Common mistakes to avoid

• Applying with a generic AI/ML research profile that does not show low-level CUDA or GPU performance engineering.
• Ignoring the energy context and treating this as a standard GPU engineering role at a tech company.
• Failing to show founding-engineer traits: ambiguity tolerance, self-direction, and willingness to build systems from zero.