Inference infrastructure Engineer

I'm hiring a Member of Technical Staff, ML Systems to join an early-stage AI infrastructure company building a new memory layer for large-scale inference.

The team is developing custom silicon and systems software that give GPUs and other AI accelerators access to a much larger high-bandwidth memory tier, with a particular focus on long-context inference, KV-cache management and reducing the cost of keeping large amounts of model state close to compute.

This is a low-level ML systems role working across inference engines, GPU performance and hardware/software co-design rather than application-layer AI.

→ Build and optimise LLM inference systems around distributed and expandable memory

→ Work across KV cache, data movement, GPU kernels and serving performance

→ Help shape the underlying hardware architecture using real workload behaviour

The team is particularly interested in engineers with experience around vLLM, SGLang, TensorRT-LLM, LMCache, CUDA or similar inference infrastructure.

If this jobs sounds like something you would be interested in, dont hesitate to reach out!