Note: The job is a remote job and is open to candidates in USA. Fuse Energy is a forward-thinking renewable energy startup on a mission to deliver a terawatt of renewable energy - fast. They are seeking a Founding AI Inference Engineer to define and build how Fuse serves AI inference workloads at scale, focusing on architecture and performance optimization.
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 a direct technical owner of inference performance and reliability
- Work closely with the CUDA and GPU engineering teams to ensure custom kernels and hardware performance work are integrated cleanly into the serving layer
- Set the standards, tooling, and benchmarks this function will run on as it grows
Skills
- 4+ years of experience 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 around architecture that's still early-stage, not joining an established one
- Experience with Triton or custom ML inference/training frameworks
- Experience with autoscaling or capacity planning for large-scale inference workloads
- Exposure to multi-tenant serving or SLA-driven infrastructure
- Background at a hyperscaler, frontier AI lab, or large-scale distributed inference system
- Familiarity with Kubernetes/Slurm for cluster orchestration
- Interest or experience in energy markets, grid systems, or sustainability-focused compute
Benefits
- 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
Company Overview
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