AI Inference Engineer

Fuse Energy, LLC; Greater London; Hybrid; GBP 120,000 - 180,000; Full time

Benefits offered by this job

Equity

Biannual bonus

Fully expensed tech

Private health insurance

Meal allowance

Job summary

Fuse Energy is building a fully integrated energy company that merges generation, hardware, grid, trading, AI, and distributed energy. We are seeking the founding engineer to own the inference serving layer, guiding performance, reliability, and deployment across a growing cluster.

You will define the serving strategy, design high-throughput workloads, and collaborate with CUDA/GPU teams to optimize throughput and cost per token while meeting ambitious uptime targets.

Qualifications

• 4+ years building or operating large-scale inference serving systems.
• Hands-on experience with inference serving frameworks and the techniques used to optimise them (batching, KV-cache management, quantisation, speculative decoding).
• Strong systems thinking across a large cluster.
• Ability to work with GPU/CUDA engineers to integrate performance work into a serving system.
• Track record of making high-stakes architecture calls and owning the outcome.
• Founding role; comfortable with ambiguity and shaping a new function.

Responsibilities

• 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, applying quantisation, distillation and speculative decoding.
• Make the core software architecture calls on serving frameworks and orchestration (e.g. vLLM, TensorRT-LLM, SGLang, Triton Inference Server).
• Translate throughput, latency and uptime commitments into concrete technical specifications and capacity plans.
• Act as direct technical owner of inference performance and reliability.
• Collaborate with CUDA/GPU engineers to integrate custom kernels and hardware performance into the serving layer.
• Set the standards, tooling and benchmarks this function will run on as it grows.

Skills

Inference serving

Architecture

Batching

KV-cache management

Quantisation

Speculative decoding

System architecture

Autoscaling

GPU collaboration

Serving frameworks

Tools

Triton Inference Server

vLLM

TensorRT-LLM

SGLang

Kubernetes

Job description

Fuse Energy is an energy startup on a mission to make energy abundant and affordable, fast. We combine first-principles thinking with cutting-edge technology to build a radically better energy system.

We've raised over $200M from top-tier investors including Balderton, Lakestar, Accel, Creandum, Lowercarbon, Ribbit, 20VC, Hummingbird and Collaborative Fund, alongside strategic angels including Nico Rosberg and GPs behind Meta, Revolut, Spotify and Uber.

We're building a fully integrated energy company: developing our own solar, batteries and other generation projects, building our own hardware, improving and developing grid infrastructure, trading power in real time, using AI across the business, and installing distributed energy in homes. By selling directly to consumers we cut out the middleman, lower costs and pass the savings on to our customers.

As data centres become one of the largest and fastest-growing sources of electricity demand, Fuse is expanding into high-performance compute infrastructure at the intersection of energy and AI. We're building the GPU/CUDA performance layer and the inference serving layer at the same time, from scratch, and we're looking for the founding engineer to own the latter. Reporting directly to the CTO, you'll own the layer above kernels and hardware: how models actually get served, scaled and delivered against committed performance targets. Few companies can pair real power delivery with real compute the way Fuse can, which puts inference serving at the heart of our offering.

Responsibilities

• 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, deciding where and how to apply quantisation, distillation, speculative decoding and similar techniques to improve throughput and cost per token, partnering with the CUDA/GPU engineers
• Make the core software architecture calls on serving frameworks and orchestration (e.g. vLLM, TensorRT-LLM, SGLang, Triton Inference Server or equivalents)
• Translate throughput, latency and uptime commitments into concrete technical specifications and serving capacity plans
• Act as direct technical owner of inference performance and reliability
• Work closely with the CUDA and GPU engineering teams to integrate custom kernels and hardware performance work cleanly into the serving layer
• Set the standards, tooling and benchmarks this function will run on as it grows

• 4+ years building or operating large-scale inference serving systems, or equivalent strong project/industry experience
• Deep, hands-on experience with inference serving frameworks and the techniques used to optimise them (batching, KV-cache management, quantisation, speculative decoding)
• Strong systems thinking, able to reason about the full path from incoming request to served response across a large cluster
• Comfortable working directly with GPU/CUDA engineers to integrate low-level performance work into a serving system
• A track record of making high-stakes architecture calls and owning the outcome
• Comfort operating without a playbook: this is a founding role shaping a new function, not joining an established one
• Bonus: Triton or custom ML inference/training frameworks; autoscaling or capacity planning for large-scale inference; multi-tenant serving or SLA-driven infrastructure; background at a hyperscaler, frontier AI lab or large-scale distributed inference system; Kubernetes/Slurm; interest in energy markets, grid systems or sustainability-focused compute

• Competitive salary and eligibility for equity
• Biannual bonus scheme
• Fully expensed tech to match your needs
• Private health insurance
• Breakfast and dinner allowance for office-based employees

As we hire globally, benefits vary by location.