Note: The job is a remote job and is open to candidates in USA. Fuse Energy is an energy startup building an integrated energy and high-performance compute business using AI and advanced infrastructure. The AI Inference Engineer will serve as the founding owner of the inference serving layer, defining its architecture, building scalable serving systems, optimizing model performance, and ensuring reliability against throughput and latency commitments.

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

Skills

• 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

• Eligibility for equity
• Biannual bonus scheme
• Fully expensed tech to match your needs
• Private health insurance
• Breakfast and dinner allowance for office-based employees
• Remote work arrangement

Company Overview