Machine Learning Engineer

Machine Learning Engineer

Want to work on the systems that make LLMs fast, scalable and efficient in production?

A Stanford-spun AI company is hiring anAI Inference Engineer to help build and optimise the infrastructure powering its production AI platform.

This is a deeply technical role focused on squeezing more performance fromGPUs, distributed systems and LLM inference at scale.

What you’ll be working on

• Architect high-performance infrastructure forLLM inference
• Optimiselatency, throughput and cost across production workloads
• Scale inference acrossmulti-GPU and multi-node environments
• Improve GPU scheduling, allocation and utilisation
• Profile bottlenecks acrosscompute, memory and networking
• Optimisebatching, quantisation, KV caching and parallelism
• Scale GPU workloads across Kubernetes infrastructure

What we’re looking for

You’ll come from anML systems, distributed computing, HPC or performance engineering background and be comfortable working close to the hardware.

StrongPython and/or C++ , PyTorch and GPU computing experience are key.

Experience withCUDA, NCCL, Triton, vLLM, TensorRT-LLM or SGLang would be particularly relevant.

You should be comfortable reasoning about howGPU utilisation, memory bandwidth, communication overhead and model architecture interact to impact inference performance.