How to apply for AI Inference Engineer

About Fuse Energy

Fuse Energy is a fully integrated energy company building solar, batteries, hardware, grid infrastructure, and real-time power trading. It has raised over $200M from investors including Balderton, Accel, and Ribbit, and is expanding into high-performance compute at the intersection of energy and AI. The company sells directly to consumers to cut out the middleman and lower costs.

About the role

This is the founding engineer role for the inference serving layer at Fuse Energy. You will own how models are served, scaled, and delivered against committed performance targets, reporting directly to the CTO. The company is building the GPU/CUDA performance layer and the inference serving layer from scratch at the same time.

A typical day

A typical day may involve working on the inference serving layer, coordinating with the GPU/CUDA performance layer, and reporting progress to the CTO. You might define or check performance targets for model delivery and scaling. The exact daily routine is not specified, so ask about team structure and working patterns during interviews.

Who Fuse Energy is looking for

• Experience building and scaling inference serving systems for large models in production.
• Strong understanding of GPU/CUDA performance and how it interacts with serving layers.
• Comfortable working from first principles in an early-stage, from-scratch environment.
• Ability to own committed performance targets and work directly with a CTO.

Tips for this application

• Highlight any experience you have building inference serving layers or model delivery systems from scratch, since this is a founding role.
• Mention specific performance targets you have met in past serving or scaling work, as the role owns committed targets.
• Reference Fuse Energy's expansion into high-performance compute and the intersection of energy and AI in your application.
• Note that the role reports directly to the CTO, so show you are comfortable with high ownership and direct communication.
• If you have worked on both GPU/CUDA performance and serving layers, make that dual experience clear.

What to cover in your cover letter

Explain why you want to build the inference serving layer from scratch at a company that also delivers real power. Describe a time you owned a serving system against performance targets. Show you understand the link between energy infrastructure and compute demand. State that you are applying for the founding inference engineer role reporting to the CTO.

Research before applying

• Read about Fuse Energy's $200M raise and its investors, including Balderton, Accel, and Ribbit.
• Look into Fuse Energy's existing work in solar, batteries, grid infrastructure, and real-time power trading.
• Understand the company's stated expansion into high-performance compute infrastructure at the intersection of energy and AI.
• Find out what is publicly known about Fuse Energy's GPU/CUDA performance layer and how the inference serving layer fits above it.

Likely interview topics

Based on the job description, expect questions about:

• How you would design an inference serving layer from scratch for a company building both GPU/CUDA and serving layers.
• Specific performance targets you have met in past serving or scaling work and how you measured them.
• How you think about the interaction between GPU/CUDA performance and the serving layer above it.
• Why Fuse Energy's combination of real power delivery and real compute is relevant to inference serving.
• How you would work directly with a CTO to own the inference serving layer as a founding engineer.

Common mistakes to avoid

• Applying without showing any experience building or scaling inference serving systems.
• Ignoring the energy and compute intersection that Fuse Energy is explicitly building around.
• Treating this as a generic AI engineer role instead of a founding inference serving role reporting to the CTO.