Senior Machine Learning Engineer, LLM Inference Optimization

About This Opportunity

We are representing a confidential AI cloud infrastructure organization seeking a Senior Machine Learning Engineer to join their Applied AI team in London. This is a hands-on role focused on LLM inference optimization, where you will own model and endpoint optimization from artifacts through production deployment.

In this position, you will work on complex optimization projects spanning model internals, inference engines, serving architecture, and benchmarking. Your focus will be on improving latency, throughput, memory efficiency, GPU utilization, and cost per token while maintaining model quality and reliability in production environments.

Responsibilities

• Own optimization work for specific model families, customer endpoints, or serving backends
• Run engine comparisons and recommend practical serving configurations for specific workloads
• Debug model quality or performance regressions during production rollouts
• Optimize LLM and VLM endpoints for latency, throughput, memory efficiency, GPU utilization, quality, and cost per token
• Deploy, configure, benchmark, and extend inference engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, or similar systems
• Build and productionize model-compression workflows, including quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery
• Implement or integrate speculative decoding, draft-model approaches, KV-cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving
• Build reproducible benchmark harnesses for TTFT, TPOT, tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token
• Partner with GPU kernel engineers and platform engineers to diagnose bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers
• Write clear design docs, performance reports, rollout plans, and customer-facing technical explanations

What We're Looking For

Must-Have Requirements:

• Strong Python and PyTorch engineering skills
• Hands-on experience deploying or optimizing LLM, VLM, or high-throughput transformer inference systems
• Practical knowledge of at least one modern inference stack such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or equivalent internal systems
• Strong understanding of transformer inference bottlenecks, including KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving
• Ability to reason quantitatively about latency, throughput, quality, utilization, and cost tradeoffs
• Strong communication skills and ability to collaborate with research, kernel, infrastructure, product, and customer teams

Nice-to-Have Qualifications:

• Experience with quantization-aware training, post-training quantization, FP8, INT8, INT4, NVFP4, MXFP4, AWQ, GPTQ, SmoothQuant, or related techniques
• Experience with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or other inference acceleration methods
• Experience with agentic workloads, including tool calling, structured outputs, streaming APIs, high concurrency, and multi-step orchestration
• CUDA or Triton familiarity, even if the role is not primarily a kernel-engineering role
• Open-source contributions to vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, KServe, or related projects

How We Work With You

When you apply through Get A Job.ai, our talent team will review your profile and conduct an initial screening. If there's a strong match, we will submit your candidacy to our client for consideration. Please note that you should not contact the client directly—all communication and coordination will be managed through our recruiting team to ensure the best experience for everyone involved.

Pay

Compensation details will be discussed during the screening process based on experience and qualifications.

Equal Opportunity: Get A Job.ai is committed to inclusive hiring practices. We welcome applications from candidates of all backgrounds and work to ensure fair consideration throughout our recruitment process.