Senior Machine Learning Engineer, LLM Inference Optimization

Accountabilities:: Own optimization initiatives for specific model families, customer endpoints, and inference serving backends. Evaluate inference engines and recommend practical serving configurations based on workload requirements. Diagnose and resolve model quality, performance, and reliability regressions during production rollouts. Optimize LLM and VLM endpoints for latency, throughput, memory efficiency, GPU utilization, model quality, and cost per token. Deploy, configure, benchmark, and extend modern inference engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, or equivalent technologies. Build and productionize model-compression workflows, including quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery. Implement or integrate advanced inference techniques such as speculative decoding, draft models, KV-cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving. Develop reproducible benchmark harnesses covering TTFT, TPOT, tokens per second per GPU, p95/p99 latency, GPU memory usage, reliability, and cost per token. Partner with GPU kernel and platform engineers to identify bottlenecks across model code, kernels, runtimes, schedulers, gateways, and cluster infrastructure. Investigate performance trade-offs quantitatively and use benchmark results to guide optimization decisions. Produce clear design documentation, performance reports, rollout plans, and technical explanations for internal and customer-facing stakeholders. Contribute to safe, measurable, and reliable production rollouts of inference improvements. Requirements: Strong software engineering skills in Python and PyTorch. Hands-on experience deploying, operating, or optimizing LLM, VLM, or high-throughput transformer inference systems. Practical experience with 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. Strong understanding of transformer inference bottlenecks, including KV cache, attention mechanisms, memory bandwidth, batching, parallelism, and long-context serving. Ability to reason quantitatively about latency, throughput, model quality, resource utilization, and cost trade-offs. Experience diagnosing complex performance problems and translating findings into production improvements. Strong communication skills and the ability to collaborate effectively with research, kernel, infrastructure, product, and customer teams. Experience with quantization-aware training, post-training quantization, FP8, INT8, INT4, NVFP4, MXFP4, AWQ, GPTQ, SmoothQuant, or related optimization techniques is a plus. Familiarity with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or other inference acceleration approaches is advantageous. Experience supporting agentic workloads involving tool calling, structured outputs, streaming APIs, high concurrency, or multi-step orchestration is a plus. Familiarity with CUDA or Triton is beneficial, even if the role is not primarily focused on kernel engineering. Contributions to open-source projects such as vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, KServe, or related technologies are advantageous. Ability to work independently, take ownership, and operate effectively in a fast-moving technical environment. Benefits: Competitive compensation. Career growth and continuous learning opportunities. Flexibility and significant ownership over technical work. Collaborative and innovative working environment. Opportunity to work on impactful AI infrastructure and inference optimization projects. Exposure to advanced LLM and VLM serving technologies and large-scale AI workloads. International environment with experienced engineering and AI teams.