Senior Software Engineer - AI Inference Performance

Details

NVIDIA is seeking a Senior Software Engineer – AI Inference Performance to advance innovative LLM and VLM inference. You will push workloads toward practical performance limits on NVIDIA GPU-accelerated systems. Your work will span models, serving software, distributed runtimes, communication, CUDA kernels, and GPU architecture, delivering measurable gains in latency, throughput, efficiency, and scale.

This is a hands-on role for an engineer who turns performance models and profiler data into working code. You will collaborate with model, framework, kernel, networking, and GPU architecture teams, contribute improvements to open-source inference engines, and develop reproducible methods that improve production deployments and future NVIDIA platforms.

Responsibilities

• Lead end-to-end analysis of LLM/VLM inference processes.
• Define representative prefill and decode workloads.
• Optimize time to first token, inter-token latency, P99 end-to-end latency, processing efficiency, and key-value (KV) cache capacity.
• For multimodal models, isolate preprocessing, encoder, and decoder costs.
• Build speed-of-light and roofline models to quantify performance headroom.
• Connect arithmetic intensity, bandwidth, occupancy, memory hierarchy, and communication costs to optimization hypotheses.
• Profile workloads using NVIDIA Nsight Systems, Nsight Compute, PyTorch Profiler, and custom instrumentation.
• Eliminate bottlenecks in host code, CUDA kernels, memory, communication, and scheduling.
• Tune serving hyperparameters and techniques such as batching, KV-cache management, quantization, speculative decoding, CUDA Graphs, and model parallelism.
• Build and optimize performance-critical kernels for attention, matrix multiplication, mixture-of-experts routing, quantization, and data movement using CUDA, CUTLASS, Triton, or related technologies.
• Establish repeatable benchmarks, canonical run records, and performance regression gates covering the model, precision, hardware, topology, software, features, and workload.
• Balance performance and accuracy.
• Collaborate across teams and contribute high-quality upgrades to TensorRT-LLM, vLLM, SGLang, or associated projects.

Requirements

• More than 6 years of experience in full-stack LLM/VLM inference performance involving models, serving, distributed runtimes, kernels, and hardware, with measurable gains in production or production-representative environments.
• Strong programming skills in Python, Rust, and/or C++, plus hands-on experience with CUDA or another GPU programming environment.
• Expertise in speed-of-light analysis, roofline models, microbenchmarks, NVIDIA Nsight Systems, and Nsight Compute.
• Ability to convert profiles into testable hypotheses and validated improvements.
• Deep understanding of GPU architecture, including Tensor Cores, memory hierarchy, caches, occupancy, synchronization, and numerical formats across hardware generations.
• Practical experience optimizing inference servers and model execution, including batching, scheduling, KV-cache management, quantization, speculative decoding, and parallelism strategies.
• Understanding of distributed systems and networking for accelerated computing, including collectives, topology, and scale-up versus scale-out performance.
• BS or MS in Computer Science, Computer Engineering, or a related field, or equivalent experience.

Preferred Qualifications

• Contributions to high-performance AI projects such as TensorRT-LLM, vLLM, SGLang, PyTorch, CUDA, Triton, or NCCL.
• Experience developing AI-agent-supported performance workflows that gather and analyze profiles, identify bottlenecks, explore serving configurations, or produce optimized runtime and kernel code.
• Experience validating generated changes through reproducible, human-reviewed tests for performance, model quality, and correctness.
• Published research, conference presentations, technical talks, or blog posts explaining inference performance methods and results.
• Experience with new LLM or VLM architectures, long-context inference, mixture-of-experts models, multimodal pipelines, or large-scale distributed serving.

Compensation And Benefits

The base salary range is USD 184,000–287,500 for Level 4 and USD 224,000–356,500 for Level 5. Base salary is determined based on location, experience, and the pay of employees in similar positions. The role also includes eligibility for equity and benefits.

Applications will be accepted at least until August 30, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes and is an equal opportunity employer.