Lead Inference Systems & Performance Engineer

United States Digital Space LLC; Greater London; On-site; GBP 101,000 - 192,000; Full time

Benefits offered by this job

Equity & Ownership

Private healthcare

Visa sponsorship

Relocation benefits

London office

Job summary

Intelligent Systems Company in London seeks a senior Inference Platform Engineer to own end‑to‑end performance for our heterogeneous inference stack. You will drive KV cache strategies, batching, memory management, and multi‑node scheduling to scale models and accelerate silicon efficiency.

The role requires deep knowledge of LLM internals, strong systems engineering, and hands‑on debugging across GPU and Linux. We offer visa sponsorship and in‑person work at our London office.

Qualifications

• Deep understanding of LLM inference internals: KV cache lifecycle, memory management, attention mechanisms, and serving architectures.
• Strong systems engineering background with proven experience optimising distributed GPU workloads.
• Proficiency in C++, CUDA, Python, Rust, or similar - and the instinct to go low‑level when it matters.
• Hands‑on debugging skills across GPU, networking, and Linux systems - able to work from first principles with limited tooling.
• Experience building or significantly optimising production‑grade, high‑throughput model serving stacks.
• Multi‑GPU and multi‑node inference optimisation using NCCL, NVLink, RDMA, InfiniBand, or RoCE.
• GPU memory profiling, CUDA or Triton kernel optimisation.
• Linux performance analysis and optimisation

Responsibilities

• Design and optimise inference serving systems across heterogeneous multi‑GPU and multi‑node environments.
• Own KV cache lifecycle management, batching strategies, and memory allocation to maximise throughput and minimise latency.
• Profile and tune GPU kernels, identify bottlenecks across compute, memory, and network, and implement targeted optimisations.
• Build and improve scheduling logic for continuous batching, disaggregated prefill/decode, and speculative decoding.
• Work with networking primitives - NCCL, NVLink, RDMA, InfiniBand, RoCE - to optimise communication across distributed inference workloads.
• Develop tooling for performance visibility, regression detection, and benchmarking across hardware configurations.

Skills

LLM inference internals

KV cache lifecycle

Memory management

High-performance systems

Distributed GPU workloads

C++

CUDA

Python

Rust

Tools

C++

CUDA

Python

Rust

Job description

Intelligent Systems Company in London seeks a senior Inference Platform Engineer to own end‑to‑end performance for our heterogeneous inference stack. You will drive KV cache strategies, batching, memory management, and multi‑node scheduling to scale models and accelerate silicon efficiency.

The role requires deep knowledge of LLM internals, strong systems engineering, and hands‑on debugging across GPU and Linux. We offer visa sponsorship and in‑person work at our London office.