Senior Machine Learning Engineer, LLM Inference Optimization
ZurichFull-timePosted 3w ago
About the role
Senior ML Engineer focusing on model and endpoint optimization for LLM/VLM inference, including quantization, caching, scheduling, and multi-node deployment to improve latency, throughput, memory efficiency, GPU utilization, and cost per token while preserving quality.
Responsibilities
- Own optimization projects for specific model families, customer endpoints, or serving backends.
- Compare inference engines and recommend serving configurations suited to individual workloads.
- Diagnose model-quality and performance regressions during production rollouts.
- Improve LLM and VLM endpoint latency, throughput, memory efficiency, GPU utilization, quality, and cost per token.
- Deploy, configure, benchmark, and extend engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, and NVIDIA Dynamo.
- Develop production model-compression workflows covering 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.
- For prefill–decode disaggregation (PDD), evaluate KV-cache transfer, worker placement, and capacity balancing to determine which workloads benefit from the architecture.
- Design and improve LLM request routers and scheduling policies that balance worker utilization, queueing, request characteristics, and KV-cache locality while meeting latency and reliability targets.
- Scale dense and mixture-of-experts inference across multiple GPU nodes. Select and tune tensor, pipeline, data, and expert parallelism—including wide expert parallelism (WideEP)—with attention to hardware topology, expert load balance, and communication overhead.
- Build reproducible benchmark harnesses measuring TTFT, TPOT, tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token. Validate architecture choices under representative traffic and consistent GPU budgets.
- Collaborate with GPU kernel and platform engineers to trace bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers.
- Produce design documents, performance reports, rollout plans, and customer-facing technical explanations.
Qualifications
- Strong engineering skills in Python and PyTorch.
- Hands-on experience deploying or optimizing LLM, VLM, or high-throughput transformer inference.
- Practical knowledge of at least one modern inference stack, such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or an equivalent internal system.
- A strong understanding of transformer inference bottlenecks involving KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving.
- Ability to quantify trade-offs among latency, throughput, quality, utilization, and cost.
- Experience designing or optimizing distributed inference system architecture, with hands-on work in one or more areas such as request routing, distributed scheduling, PDD, multi-node inference, or expert parallelism.
- Strong communication and collaboration skills across research, kernel, infrastructure, product, and customer teams.
Skills
Required:PythonPyTorchLLM/VLM inference optimizationInference stacksDistributed inferenceKV-cache optimizationBenchmarkingCommunication
Benefits
- Competitive compensation
- Career growth and learning opportunities
- Flexibility and ownership
- Collaborative and innovative culture
- Opportunity to work on impactful AI projects
- International environment and talented teams
Last updated Oct 7, 2026. Interested? Apply on the company site.
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