AI Inference Engineer

• Define Fuse's inference serving strategy and architecture from first principles.
• Design and build the serving stack: request routing, batching, scheduling, and autoscaling for high-throughput, latency-sensitive inference workloads.
• Own model-level optimisation strategy for serving, including quantisation, distillation, speculative decoding, and similar techniques.
• Act as a direct technical owner of inference performance and reliability, working closely with CUDA and GPU engineering teams.

This role enables Fuse to deliver high-performance AI inference at scale, pairing real power delivery with real compute to accelerate renewable energy adoption and reduce the carbon footprint of AI workloads.

• 4+ years of experience building or operating large-scale inference serving systems.
• Deep, hands-on experience with inference serving frameworks and optimisation techniques (batching, KV-cache management, quantisation, speculative decoding).
• Strong systems thinking and ability to reason about the full path from incoming request to served response across a large cluster.
• Comfortable working directly with GPU/CUDA engineers and making high-stakes architecture calls.

• 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.

Fuse Energy is a forward-thinking renewable energy startup on a mission to deliver a terawatt of renewable energy fast, combining first-principles thinking with cutting-edge technology to build a radically better energy system.