AI Inference Engineer
Fuse Energy Open · verified Oct 22, 2026 At a glance
Mid-level ML Platform & Ops role at Fuse Energy. Remote, United Kingdom · full-time.
Pay not stated ML Platform & Ops salary
aimlroles summary, based on the employer's posting.
What you'll do
- Set the serving architecture, then build routing, batching, scheduling and autoscaling for demanding inference workloads
- Choose model optimisations such as quantisation and speculative decoding to raise throughput and lower token costs
- Turn throughput, latency and uptime promises into technical requirements and serving capacity plans
- Own inference speed and reliability, integrating kernel and hardware improvements with CUDA and GPU engineers
- Establish the benchmarks and tools the serving function will use as it grows
What you bring
- At least four years building or running large-scale inference serving systems, or equivalent substantial project or industry experience
- Hands-on knowledge of inference frameworks and serving optimisations, including batching, KV-cache handling and quantisation
- You can reason across the full request-to-response path in a large cluster and work directly with GPU and CUDA engineers
- You have made consequential architecture decisions and taken responsibility for their results
Who this fits
This founding, mid-level role suits an engineer ready to shape an inference-serving function from the ground up. You’ll report to the CTO and work closely with CUDA and GPU engineers. The position is listed as remote in London, United Kingdom.
From the employer
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
Requirements
- 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
Benefits
- 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.